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Fatty acid amide hydrolase (FAAH) hydrolyzes several bioactive lipids including the endocannabinoid anandamide. Synthetic FAAH inhibitors are being generated to help define the biological role(s) of this enzyme, the lipids it degrades in vivo, and the disease states that might benefit from its pharmacological modulation. AZ513 inhibits human FAAH (IC(50)=551 nM), is 20-fold more potent against rat FAAH (IC(50)=27 nM), and is inactive at 10 µM against the serine hydrolases acetylcholinesterase, thrombin, and trypsin. In contrast to most other potent FAAH inhibitors, AZ513 showed no evidence of covalently modifying the enzyme and displayed reversible inhibition. In an enzyme cross-competition assay, AZ513 did not compete with OL-135, an inhibitor that binds to the catalytic site in FAAH, which indicates that AZ513 does not bind to the catalytic site and is therefore noncompetitive with respect to substrate. AZ513 has good cell penetration as demonstrated by inhibition of anandamide hydrolysis in human FAAH-transfected HEK293 cells (IC(50)=360 nM). AZ513 was tested in a rat spinal cord slice preparation where CB(1) activation reduces excitatory post-synaptic currents (EPSCs). In this native tissue assay of synaptic activity, AZ513 reduced EPSCs, which is consistent with inhibiting endogenous FAAH and augmenting endocannabinoid tone. AZ513 has a unique biochemical profile compared with other published FAAH inhibitors and will be a useful tool compound to further explore the role of FAAH in various biological processes.

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Initial high throughput screening efforts identified highly potent and selective kappa opioid receptor antagonist 3 (κ IC(50)=77 nM; µ:κ and δ:κ IC(50) ratios>400) which lacked CNS exposure in vivo. Modification of this scaffold resulted in development of a series of 8-azabicyclo[3.2.1]octan-3-yloxy-benzamides showing potent and selectivity κ antagonism as well as good brain exposure. Analog 6c (κ IC(50)=20 nM; µ:κ=36, δ:κ=415) was also shown to reverse κ-agonist induced rat diuresis in vivo.

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Fragment-based lead generation was applied to find novel small-molecule inhibitors of beta-secretase (BACE-1), a key target for the treatment of Alzheimer's disease. Fragment hits coming from a 1D NMR screen were characterized by BIAcore, and the most promising compounds were soaked into protein crystals to help the rational design of more potent hit analogues. Problems arising due to our inability to grow BACE-1 crystals at the biologically relevant pH at which the screen was run were overcome by using endothiapepsin as a surrogate aspartyl protease. Among others, we identified 6-substituted isocytosines as a novel warhead against BACE-1, and the accompanying paper in this journal describes how these were optimized to a lead series of nanomolar inhibitors.1.

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Fragment-based lead generation has led to the discovery of a novel series of cyclic amidine-based inhibitors of beta-secretase (BACE-1). Initial fragment hits with an isocytosine core having millimolar potency were identified via NMR affinity screening. Structure-guided evolution of these fragments using X-ray crystallography together with potency determination using surface plasmon resonance and functional enzyme inhibition assays afforded micromolar inhibitors. Similarity searching around the isocytosine core led to the identification of a related series of inhibitors, the dihydroisocytosines. By leveraging the knowledge of the ligand-BACE-1 recognition features generated from the isocytosines, the dihydroisocytosines were efficiently optimized to submicromolar potency. Compound 29, with an IC50 of 80 nM, a ligand efficiency of 0.37, and cellular activity of 470 nM, emerged as the lead structure for future optimization.

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Fragment-based lead generation (FBLG) has recently emerged as an alternative to traditional high throughput screening (HTS) to identify initial chemistry starting points for drug discovery programs. In comparison to HTS screening libraries, the screening sets for FBLG tend to contain orders of magnitude fewer compounds, and the compounds themselves are less structurally complex and have lower molecular weight. This report summarises the advent of FBLG within the industry and then describes the FBLG experience at AstraZeneca. We discuss (1) optimising the design of screening libraries, (2) hit detection methodologies, (3) evaluation of hit quality and use of ligand efficiency calculations, and (4) approaches to evolve fragment-based, low complexity hits towards drug-like leads. Furthermore, we exemplify our use of FBLG with case studies in the following drug discovery areas: antibacterial enzyme targets, GPCRs (melanocortin 4 receptor modulators), prostaglandin D2 synthase inhibitors, phosphatase inhibitors (protein tyrosine phosphotase 1B), and protease inhibitors (b-secretase).

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Fragment-based lead discovery has been successfully applied to the aspartyl protease enzyme beta-secretase (BACE-1). Fragment hits that contained an aminopyridine motif binding to the two catalytic aspartic acid residues in the active site of the enzyme were the chemical starting points. Structure-based design approaches have led to identification of low micromolar lead compounds that retain these interactions and additionally occupy adjacent hydrophobic pockets of the active site. These leads form two subseries, for which compounds 4 (IC50 = 25 microM) and 6c (IC50 = 24 microM) are representative. In the latter series, further optimization has led to 8a (IC50 = 690 nM).

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Serine peptidases are a large, well-studied, and medically important class of peptidases. Despite the attention these enzymes have received, details concerning the substrate specificity of even some of the best known enzymes in this class are lacking. One approach to rapidly characterizing substrate specificity for peptidases is the use of positional scanning combinatorial substrate libraries. We recently synthesized such a library for enzymes with a preference for arginine at P1 and demonstrated the use of this library with thrombin (Edwards et al. Bioorg. Med. Chem. Lett. 2000, 10, 2291). In the present work, we extend these studies by demonstrating good agreement between the theroretical and measured content of portions of this library and by showing that the library permits rapid characterization of the substrate specificity of additional SA clan serine peptidases including factor Xa, tryptase, and trypsin. These results were consistent both with cleavage sites in natural substrates and cleavage of commercially available synthetic substrates. We also demonstrate that pH or salt concentration have a quantitative effect on the rate of cleavage of the pooled library substrates but that correct prediction of optimal substrates for the enzymes studied appeared to be independent of these parameters. These studies provide new substrate specificity data on an important class of peptidases and are the first to provide physical characterization of a peptidase substrate library.

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Cerebral deposition of amyloid beta-protein (A beta) is believed to play a key role in the pathogenesis of Alzheimer's disease. Because A beta is produced from the processing of amyloid beta-protein precursor (APP) by beta- and gamma-secretases, these enzymes are considered important therapeutic targets for identification of drugs to treat Alzheimer's disease. Unlike beta-secretase, which is a monomeric aspartyl protease, gamma-secretase activity resides as part of a membrane-bound, high molecular weight, macromolecular complex. Pepstatin and L685458 are among several structural classes of gamma-secretase inhibitors identified so far. These compounds possess a hydroxyethylene dipeptide isostere of aspartyl protease transition state analogs, suggesting gamma-secretase may be an aspartyl protease. However, the mechanism of inhibition of gamma-secretase by pepstatin and L685458 has not been elucidated. In this study, we report that pepstatin A methylester and L685458 unexpectedly displayed linear non-competitive inhibition of gamma-secretase. Sulfonamides and benzodiazepines, which do not resemble transition state analogs of aspartyl proteases, also displayed potent, non-competitive inhibition of gamma-secretase. Models to rationalize how transition state analogs inhibit their targets by non-competitive inhibition are discussed.

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