Publications by authors named "Yael Marantz"

Laquinimod is an oral drug currently being evaluated for the treatment of relapsing, remitting, and primary progressive multiple sclerosis and Huntington's disease. Laquinimod exerts beneficial activities on both the peripheral immune system and the CNS with distinctive changes in CNS resident cell populations, especially astrocytes and microglia. Analysis of genome-wide expression data revealed activation of the aryl hydrocarbon receptor (AhR) pathway in laquinimod-treated mice.

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Optimization of a benzofuranyl S1P1 agonist lead compound (3) led to the discovery of 1-(3-fluoro-4-(5-(2-fluorobenzyl)benzo[d]thiazol-2-yl)benzyl)azetidine-3-carboxylic acid (14), a potent S1P1 agonist with minimal activity at S1P3. Dosed orally at 0.3 mg/kg, 14 significantly reduced blood lymphocyte counts 24 h postdose and attenuated a delayed type hypersensitivity (DTH) response to antigen challenge.

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We have discovered novel benzofuran-based S1P1 agonists with excellent in vitro potency and selectivity. 1-((4-(5-Benzylbenzofuran-2-yl)-3-fluorophenyl)methyl) azetidine-3-carboxylic acid (18) is a potent S1P1 agonist with >1000× selectivity over S1P3. It demonstrated a good in vitro ADME profile and excellent oral bioavailability across species.

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Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability.

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In silico (or virtual) screening has become a common practice in current computer-aided drug design efforts. However, application to hit discovery in the G Protein-Coupled Receptors (GPCRs) arena was until recently hampered by the paucity of crystal structures available for this important class of pharmaceutical targets, forcing practitioners in the field to rely on GPCR models derived either ab initio or through homology modeling approaches. In this work we describe the EPIX in silico screening workflow which consists of the following stages: (1) Target modeling; (2) Preparation of screening library; (3) Docking; (4) Binding mode selection; (5) Scoring; (6) Consensus scoring and (7) Selection of virtual hits.

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Identifying active compounds (hits) that bind to biological targets of pharmaceutical relevance is the cornerstone of drug design efforts. Structure based virtual screening, namely, the in silico evaluation of binding energies and geometries between a protein and its putative ligands, has emerged over the past few years as a promising approach in this field. The success of the method relies on the availability of reliable 3-dimensional (3D) structures of the target protein and its candidate ligands (the screening library), a reliable docking method that can fit the different ligands into the protein's binding site, and an accurate scoring function that can rank the resulting binding modes in accord with their binding affinities.

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Recent evidence suggests that 5-hydroxytryptamine (5-HT)(4) receptor activity enhances cognition and provides neuroprotection. Here we report the effects of VRX-03011, a novel partial 5-HT(4) agonist, that is both potent (K(i) approximately 30 nM) and highly selective (K(i) > 5 microM for all other 5-HT receptors tested). In separate experiments, rats received VRX-03011 (0.

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We report the discovery of a novel, potent, and selective amidosulfonamide nonazapirone 5-HT1A agonist for the treatment of anxiety and depression, which is now in Phase III clinical trials for generalized anxiety disorder (GAD). The discovery of 20m (PRX-00023), N-{3-[4-(4-cyclohexylmethanesulfonylaminobutyl)piperazin-1-yl]phenyl}acetamide, and its backup compounds, followed a new paradigm, driving the entire discovery process with in silico methods and seamlessly integrating computational chemistry with medicinal chemistry, which led to a very rapid discovery timeline. The program reached clinical trials within less than 2 years from initiation, spending less than 6 months in lead optimization with only 31 compounds synthesized.

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G-protein coupled receptors (GPCRs) are a major group of drug targets for which only one x-ray structure is known (the nondrugable rhodopsin), limiting the application of structure-based drug discovery to GPCRs. In this paper we present the details of PREDICT, a new algorithmic approach for modeling the 3D structure of GPCRs without relying on homology to rhodopsin. PREDICT, which focuses on the transmembrane domain of GPCRs, starts from the primary sequence of the receptor, simultaneously optimizing multiple 'decoy' conformations of the protein in order to find its most stable structure, culminating in a virtual receptor-ligand complex.

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The application of structure-based in silico methods to drug discovery is still considered a major challenge, especially when the x-ray structure of the target protein is unknown. Such is the case with human G protein-coupled receptors (GPCRs), one of the most important families of drug targets, where in the absence of x-ray structures, one has to rely on in silico 3D models. We report repeated success in using ab initio in silico GPCR models, generated by the predict method, for blind in silico screening when applied to a set of five different GPCR drug targets.

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G protein-coupled receptors (GPCRs) are membrane-embedded proteins responsible for signal transduction; these receptors are, therefore, among the most important pharmaceutical drug targets. In the absence of X-ray structures, there have been numerous attempts to model the three-dimensional (3D) structure of GPCRs. In this review, the current status of GPCR modeling is evaluated, highlighting recent progress made in rhodopsin-based homology modeling and de novo modeling technology.

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