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Retinol-binding protein 4 (RBP4) serves as a transporter for all- trans-retinol (1) in the blood, and it has been proposed to act as an adipokine. Elevated plasma levels of the protein have been linked to diabetes, obesity, cardiovascular diseases, and nonalcoholic fatty liver disease (NAFLD). Recently, adipocyte-specific overexpression of RBP4 was reported to cause hepatic steatosis in mice. We previously identified an orally bioavailable RBP4 antagonist that significantly lowered RBP4 serum levels in Abca4-/- knockout mice with concomitant normalization of complement system protein expression and reduction of bisretinoid formation within the retinal pigment epithelium. We describe herein the discovery of novel RBP4 antagonists 48 and 59, which reduce serum RBP4 levels by >80% in mice upon acute oral dosing. Furthermore, 59 demonstrated efficacy in the transgenic adi-hRBP4 murine model of hepatic steatosis, suggesting that RBP4 antagonists may also have therapeutic utility for the treatment of NAFLD.

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The emergence of multidrug-resistant bacteria necessitates the identification of unique targets of intervention and compounds that inhibit their function. Gram-positive bacteria use a well-conserved tRNA-responsive transcriptional regulatory element in mRNAs, known as the T-box, to regulate the transcription of multiple operons that control amino acid metabolism. T-box regulatory elements are found only in the 5'-untranslated region (UTR) of mRNAs of Gram-positive bacteria, not Gram-negative bacteria or the human host. Using the structure of the 5'UTR sequence of the Bacillus subtilis tyrosyl-tRNA synthetase mRNA T-box as a model, in silico docking of 305 000 small compounds initially yielded 700 as potential binders that could inhibit the binding of the tRNA ligand. A single family of compounds inhibited the growth of Gram-positive bacteria, but not Gram-negative bacteria, including drug-resistant clinical isolates at minimum inhibitory concentrations (MIC 16-64 µg mL-1 ). Resistance developed at an extremely low mutational frequency (1.21×10-10 ). At 4 µg mL-1 , the parent compound PKZ18 significantly inhibited in vivo transcription of glycyl-tRNA synthetase mRNA. PKZ18 also inhibited in vivo translation of the S. aureus threonyl-tRNA synthetase protein. PKZ18 bound to the Specifier Loop in vitro (Kd ≈24 µm). Its core chemistry necessary for antibacterial activity has been identified. These findings support the T-box regulatory mechanism as a new target for antibiotic discovery that may impede the emergence of resistance.

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BET proteins are key epigenetic regulators that regulate transcription through binding to acetylated lysine (AcLys) residues of histones and transcription factors through bromodomains (BDs). The disruption of this interaction with small molecule bromodomain inhibitors is a promising approach to treat various diseases including cancer, autoimmune and cardiovascular diseases. Covalent inhibitors can potentially offer a more durable target inhibition leading to improved in vivo pharmacology. Here we describe the design of covalent inhibitors of BRD4(BD1) that target a methionine in the binding pocket by attaching an epoxide warhead to a suitably oriented noncovalent inhibitor. Using thermal denaturation, MALDI-TOF mass spectrometry, and an X-ray crystal structure, we demonstrate that these inhibitors selectively form a covalent bond with Met149 in BRD4(BD1) but not other bromodomains and provide durable transcriptional and antiproliferative activity in cell based assays. Covalent targeting of methionine offers a novel approach to drug discovery for BET proteins and other targets.

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Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20 years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

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Mathematical models of metabolism from bacterial systems biology have proven their utility across multiple fields, for example metabolic engineering, growth phenotype simulation, and biological discovery. The usefulness of the models stems from their ability to compute a link between genotype and phenotype, but their ability to accurately simulate gene-gene interactions has not been investigated extensively. Here we assess how accurately a metabolic model for Escherichia coli computes one particular type of gene-gene interaction, synthetic lethality, and find that the accuracy rate is between 25% and 43%. The most common failure modes were incorrect computation of single gene essentiality and biological information that was missing from the model. Moreover, we performed virtual and biological screening against several synthetic lethal pairs to explore whether two-compound formulations could be found that inhibit the growth of Gram-negative bacteria. One set of molecules was identified that, depending on the concentrations, inhibits E. coli and S. enterica serovar Typhimurium in an additive or antagonistic manner. These findings pinpoint specific ways in which to improve the predictive ability of metabolic models, and highlight one potential application of systems biology to drug discovery and translational medicine.

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Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100-200 µM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.

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Antagonists of retinol-binding protein 4 (RBP4) impede ocular uptake of serum all-trans retinol (1) and have been shown to reduce cytotoxic bisretinoid formation in the retinal pigment epithelium (RPE), which is associated with the pathogenesis of both dry age-related macular degeneration (AMD) and Stargardt disease. Thus, these agents show promise as a potential pharmacotherapy by which to stem further neurodegeneration and concomitant vision loss associated with geographic atrophy of the macula. We previously disclosed the discovery of a novel series of nonretinoid RBP4 antagonists, represented by bicyclic [3.3.0]-octahydrocyclopenta[c]pyrrolo analogue 4. We describe herein the utilization of a pyrimidine-4-carboxylic acid fragment as a suitable isostere for the anthranilic acid appendage of 4, which led to the discovery of standout antagonist 33. Analogue 33 possesses exquisite in vitro RBP4 binding affinity and favorable drug-like characteristics and was found to reduce circulating plasma RBP4 levels in vivo in a robust manner (>90%).

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Bromodomains are key transcriptional regulators that are thought to be druggable epigenetic targets for cancer, inflammation, diabetes and cardiovascular therapeutics. Of particular importance is the first of two bromodomains in bromodomain containing 4 protein (BRD4(1)). Protein-ligand docking in BRD4(1) was used to purchase a small, focused screening set of compounds possessing a large variety of core structures. Within this set, a small number of weak hits each contained a dihydroquinoxalinone ring system. We purchased other analogs with this ring system and further validated the new hit series and obtained improvement in binding inhibition. Limited exploration by new analog synthesis showed that the binding inhibition in a FRET assay could be improved to the low µM level making this new core a potential hit-to-lead series. Additionally, the predicted geometries of the initial hit and an improved analog were confirmed by X-ray co-crystallography with BRD4(1).

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Through medicinal chemistry lead optimization studies focused on calculated properties and guided by X-ray crystallography and computational modeling, potent pan-JNK inhibitors were identified that showed submicromolar activity in a cellular assay. Using in vitro ADME profiling data, 9t was identified as possessing favorable permeability and a low potential for efflux, but it was rapidly cleared in liver microsomal incubations. In a mouse pharmacokinetics study, compound 9t was brain-penetrant after oral dosing, but exposure was limited by high plasma clearance. Brain exposure at a level expected to support modulation of a pharmacodynamic marker in mouse was achieved when the compound was coadministered with the pan-cytochrome P450 inhibitor 1-aminobenzotriazole.

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Accumulation of lipofuscin in the retina is associated with pathogenesis of atrophic age-related macular degeneration and Stargardt disease. Lipofuscin bisretinoids (exemplified by N-retinylidene-N-retinylethanolamine) seem to mediate lipofuscin toxicity. Synthesis of lipofuscin bisretinoids depends on the influx of retinol from serum to the retina. Compounds antagonizing the retinol-dependent interaction of retinol-binding protein 4 (RBP4) with transthyretin in the serum would reduce serum RBP4 and retinol and inhibit bisretinoid formation. We recently showed that A1120 (3), a potent carboxylic acid based RBP4 antagonist, can significantly reduce lipofuscin bisretinoid formation in the retinas of Abca4(-/-) mice. As part of the NIH Blueprint Neurotherapeutics Network project we undertook the in vitro exploration to identify novel conformationally flexible and constrained RBP4 antagonists with improved potency and metabolic stability. We also demonstrate that upon acute and chronic dosing in rats, 43, a potent cyclopentyl fused pyrrolidine antagonist, reduced circulating plasma RBP4 protein levels by approximately 60%.

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The sodium glucose co-transporter 2 (SGLT2) has received considerable attention in recent years as a target for the treatment of type 2 diabetes mellitus. This report describes the design, synthesis and structure-activity relationship (SAR) of C-glycosides with benzyltriazolopyridinone and phenylhydantoin as the aglycone moieties as novel SGLT2 inhibitors. Compounds 5p and 33b demonstrated high potency in inhibiting SGLT2 and high selectivity against SGLT1. The in vitro ADMET properties of these compounds will also be discussed.

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Novel 4-phenyl tetrahydroisoquinolines that inhibit both dopamine and norepinephrine transporters were designed and prepared. In this Letter, we describe the synthesis, in vitro activity and associated structure-activity relationships of this series. We also report the ex vivo NET occupancy of a representative compound, 41.

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Early drug discovery processes rely on hit finding procedures followed by extensive experimental confirmation in order to select high priority hit series which then undergo further scrutiny in hit-to-lead studies. The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds. We describe the steps in typical hit identification and hit-to-lead programs and then describe how cheminformatic analysis assists this process. In particular, scaffold analysis, clustering and property calculations assist in the design of high-throughput screening libraries, the early analysis of hits and then organizing compounds into series for their progression from hits to leads. Additionally, these computational tools can be used in virtual screening to design hit-finding libraries and as procedures to help with early SAR exploration.

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A series of novel hydroxamic acid based histone deacetylases (HDAC) inhibitors with aryl ether and aryl sulfone residues at the terminus of a substituted, unsaturated 5-carbon spacer moiety have been synthesized for the first time and evaluated. Compounds with meta- and para-substitution on the aryl ring of ether hydroxamic acids 19c, 20c, 19e, 19f and 19g are potent HDAC inhibitors with activities at low nanomolar levels.

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An indazole based series of glucocorticoid receptor agonists is reported. The SAR exploration of this scaffold yielded compounds with nanomolar affinity for the glucocorticoid receptor with indications of selectivity for the preferred transrepression mechanism; in vivo efficacy was observed in the mouse LPS induced TNFalpha model for compound 28.

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Human intestinal absorption (HIA) is an important roadblock in the formulation of new drug substances. Computational models are needed for the rapid estimation of this property. The measurements are determined via in vivo experiments or in vitro permeability studies. We present several computational models that are able to predict the absorption of drugs by the human intestine and the permeability through human Caco-2 cells. The training and prediction sets were derived from literature sources and carefully examined to eliminate compounds that are actively transported. We compare our results to models derived by other methods and find that the statistical quality is similar. We believe that models derived from both sources of experimental data would provide greater consistency in predictions. The performance of several QSPR models that we investigated to predict outside the training set for either experimental property clearly indicates that caution should be exercised while applying any of the models for quantitative predictions. However, we are able to show that the qualitative predictions can be obtained with close to a 70% success rate.

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Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization. Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors. Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes. Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches.

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The blood-brain permeation of a structurally diverse set of 281 compounds was modeled using linear regression and a multivariate genetic partial least squares (G/PLS) approach. Key structural features affecting the logarithm of blood-brain partitioning (logBB) were captured through statistically significant quantitative structure-activity relationship (QSAR) models. These relationships reveal the importance of logP, polar surface area, and a variety of electrotopological indices for accurate predictions of logBB. The best models reveal an excellent correlation (r > 0.9) for a training set of 58 compounds. Likewise, the comparison of the average logBB values obtained from an ensemble of QSAR models with experimental values also verifies the statistical quality of the models (r > 0.9). The models provide good agreement (r approximately 0.7) between the predicted logBB values for 34 molecules in the external validation set and the experimental values. To further validate the models for use during the drug discovery process, a prediction set of 181 drugs with reported CNS penetration data was used. A >70% success rate is obtained by using any of the QSAR models in the qualitative prediction for CNS permeable (active) drugs. A lower success rate (approximately 60%) was obtained for the best model for CNS impermeable (inactive) drugs. Combining the predictions obtained from all the models (consensus) did not significantly improve the discrimination of CNS active and CNS inactive molecules. Finally, using the therapeutic classification as a guiding tool, the CNS penetration capability of over 2000 compounds in the Synthline database was estimated. The results were very similar to the smaller set of 181 compounds.

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A method termed Median Partitioning (MP) has been developed to select diverse sets of molecules from large compound pools. Unlike many other methods for subset selection, the MP approach does not depend on pairwise comparison of molecules and can therefore be applied to very large compound collections. The only time limiting step is the calculation of molecular descriptors for database compounds. MP employs arrays of property descriptors with little correlation to divide large compound pools into partitions from which representative molecules can be selected. In each of n subsequent steps, a population of molecules is divided into subpopulations above and below the median value of a property descriptor until a desired number of 2n partitions are obtained. For descriptor evaluation and selection, an entropy formulation was embedded in a genetic algorithm. MP has been applied here to generate a subset of the Available Chemicals Directory, and the results have been compared with cell-based partitioning.

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