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-a (DAGL-a) is an intracellular, multidomain protein responsible for the formation of the endocannabinoid 2-arachidonoylglycerol (2-AG) in the central nervous system. [1] 2-AG is an endogenous signaling lipid that interacts with the cannabinoid CB1 and CB2 receptors. [2] Little is known about the regulation of its biosynthetic pathway and it is largely unclear to what extent 2-AG is responsible for distinct cannabinoid CB1 receptor mediated biological processes. Selective inhibitors of DAGL-a may contribute to a more fundamental understanding of the physiological role of 2-AG and may serve as potential drug candidates for the treatment of obesity and neurodegenerative diseases. [3] Currently, there are no selective inhibitors and activity-based probes available for the study of DAGL-a. [4] The identification of selective DAGL-a inhibitors is hampered by a lack of structural knowledge of the target, and a lack of assays that make use of endogenous DAGL-a activity in proteomes. No crystal structures are available and no homology models have been reported to aid hit identification and to guide optimization of the inhibitors. Determination of the selectivity of the inhibitors in native tissues is important because DAGL-a belongs to the serine hydrolase family, which contains more than 200 members with various physiological functions. [5] Fluorophosphonate (FP)based probes are routinely employed in competitive activitybased protein profiling (ABPP) experiments to determine the selectivity of serine hydrolase inhibitors in complex proteomes. DAGL-a, however, does not react with these activitybased probes. [6] Therefore, a new probe that can label native DAGL-a would be of value for studying the potency and selectivity of novel DAGL-a inhibitors in brain proteomes.Here we present a strategy that combines a knowledge-based in silico design approach and the development of a novel activity-based probe (ABP), based on the nonselective DAGL-a inhibitor tetrahydrolipstatin (THL; also known as Orlistat, a drug used for the treatment of obesity). This strategy resulted in the rapid identification of DAGL-a inhibitors with a new chemotype and high selectivity in the brain proteome.To identify novel DAGL-a inhibitors, we built a pharmacophore model based on THL using Discovery Studio Software Suite from Accelrys. Since THL can assume many different conformations, we searched the protein crystallographic database for crystal structures with a bioactive conformation for THL. A cocrystal structure of THL with fatty acid synthase (pdb-code: 2PX6) was identified (Figure 1 A) [7] that contains the same Ser-His-Asp catalytic triad and typical a/b hydrolase fold motif as DAGL-a. In this cocrystal structure, the nucleophilic Ser of the enzyme is covalently attached to the carbonyl moiety of the lactone. We reconstituted the ester to form the b-lactone to recover the active warhead of THL. After optimization of the geometry of the lactone, the resulting conformation was used to generate two pharmacophore models (Figure...

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 .

Activity-based protein profiling (ABPP) has emerged as a valuable chemical proteomics method to guide the therapeutic development of covalent drugs by assessing their on-target engagement and off-target activity. We recently used ABPP to determine the serine hydrolase interaction landscape of the experimental drug BIA 10-2474, thereby providing a potential explanation for the adverse side effects observed with this compound. ABPP allows mapping of protein interaction landscapes of inhibitors in cells, tissues and animal models. Whereas our previous protocol described quantification of proteasome activity using stable-isotope labeling, this protocol describes the procedures for identifying the in vivo selectivity profile of covalent inhibitors with label-free quantitative proteomics. The optimization of our protocol for label-free quantification methods results in high proteome coverage and allows the comparison of multiple biological samples. We demonstrate our protocol by assessing the protein interaction landscape of the diacylglycerol lipase inhibitor DH376 in mouse brain, liver, kidney and testes. The stages of the protocol include tissue lysis, probe incubation, target enrichment, sample preparation, liquid chromatography-mass spectrometry (LC-MS) measurement, data processing and analysis. This approach can be used to study target engagement in a native proteome and to identify potential off targets for the inhibitor under investigation. The entire protocol takes at least 4 d, depending on the number of samples.

Eine Modellmethode: Eine Strategie zur Wirkstoffsuche kombiniert rationales Design am Computer mit der Entwicklung neuartiger aktivitätsbasierter Sonden (ABPs) für den Nachweis von endogener Diacylglycerollipase‐α (DAGL‐α). Der Ansatz führte zur raschen Identifizierung von neuen DAGL‐α‐Hemmern mit hoher Selektivität im Gehirnproteom. ABPP=aktivitätsbasiertes Protein‐Profiling.