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PubMed | Mnemosyne Pharmaceuticals Inc., Neuroservice, Boston Childrens Hospital, Aptuit Medicines Research Center and 2 more.
Type: Journal Article | Journal: PloS one | Year: 2016

GluN2A is the most abundant of the GluN2 NMDA receptor subunits in the mammalian CNS. Physiological and genetic evidence implicate GluN2A-containing receptors in susceptibility to autism, schizophrenia, childhood epilepsy and neurodevelopmental disorders such as Rett Syndrome. However, GluN2A-selective pharmacological probes to explore the therapeutic potential of targeting these receptors have been lacking. Here we disclose a novel series of pyrazine-containing GluN2A antagonists exemplified by MPX-004 (5-(((3-chloro-4-fluorophenyl)sulfonamido)methyl)-N-((2-methylthiazol-5-yl)methyl)pyrazine-2-carboxamide) and MPX-007 (5-(((3-fluoro-4-fluorophenyl)sulfonamido)methyl)-N-((2-methylthiazol-5-yl)methyl)methylpyrazine-2-carboxamide). MPX-004 and MPX-007 inhibit GluN2A-containing NMDA receptors expressed in HEK cells with IC50s of 79 nM and 27 nM, respectively. In contrast, at concentrations that completely inhibited GluN2A activity these compounds have no inhibitory effect on GluN2B or GluN2D receptor-mediated responses in similar HEK cell-based assays. Potency and selectivity were confirmed in electrophysiology assays in Xenopus oocytes expressing GluN2A-D receptor subtypes. Maximal concentrations of MPX-004 and MPX-007 inhibited ~30% of the whole-cell current in rat pyramidal neurons in primary culture and MPX-004 inhibited ~60% of the total NMDA receptor-mediated EPSP in rat hippocampal slices. GluN2A-selectivity at native receptors was confirmed by the finding that MPX-004 had no inhibitory effect on NMDA receptor mediated synaptic currents in cortical slices from GRIN2A knock out mice. Thus, MPX-004 and MPX-007 offer highly selective pharmacological tools to probe GluN2A physiology and involvement in neuropsychiatric and developmental disorders.


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PubMed | Bharathidasan University and Jubilant Biosys Ltd and 96
Type: Journal Article | Journal: Organic & biomolecular chemistry | Year: 2016

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PubMed | Jubilant Biosys Ltd and 96, Norgine Ltd and Jubilant Chemsys Ltd
Type: Journal Article | Journal: Journal of medicinal chemistry | Year: 2016

Chemokine receptor 9 (CCR9), a cell surface chemokine receptor which belongs to the G protein-coupled receptor, 7-trans-membrane superfamily, is expressed on lymphocytes in the circulation and is the key chemokine receptor that enables these cells to target the intestine. It has been proposed that CCR9 antagonism represents a means to prevent the aberrant immune response of inflammatory bowel disease in a localized and disease specific manner and one which is accessible to small molecule approaches. One possible reason why clinical studies with vercirnon, a prototype CCR9 antagonist, were not successful may be due to a relatively poor pharmacokinetic (PK) profile for the molecule. We wish to describe work aimed at producing new, orally active CCR9 antagonists based on the 1,3-dioxoisoindoline skeleton. This study led to a number of compounds that were potent in the nanomolar range and which, on optimization, resulted in several possible preclinical development candidates with excellent PK properties.


Gehling V.S.,Constellation Pharmaceuticals | Hewitt M.C.,Constellation Pharmaceuticals | Vaswani R.G.,Constellation Pharmaceuticals | Leblanc Y.,Constellation Pharmaceuticals | And 20 more authors.
ACS Medicinal Chemistry Letters | Year: 2013

The identification of a novel series of small molecule BET inhibitors is described. Using crystallographic binding modes of an amino-isoxazole fragment and known BET inhibitors, a structure-based drug design effort lead to a novel isoxazole azepine scaffold. This scaffold showed good potency in biochemical and cellular assays and oral activity in an in vivo model of BET inhibition. © 2013 American Chemical Society.


Viswanadhan V.N.,Jubilant Biosys Ltd and 96 | Rajesh H.,Jubilant Biosys Ltd and 96 | Balaji V.N.,Jubilant Biosys Ltd and 96
ACS Combinatorial Science | Year: 2011

A new characterization of known drug, lead, and representative nondrug databases was performed taking into account several properties at the atomic and molecular levels. This characterization included atom type preferences, intrinsic structural diversity (Atom Type Diversity, ATD), and other well-known physicochemical properties, as an approach for rapid assessment of druglikeness for small molecule libraries. To characterize ATD, an elaborate united atom classification, UALOGP (United Atom Log P), with 148 atom types, was developed along with associated atomic physicochemical parameters. This classification also enabled an analysis of atom type and physicochemical property distributions (for calculated log P, molar refractivity, molecular weight, total atom count, and ATD) of drug, lead, and nondrug databases, a reassessment of the Ro5 (Rule of Five) and GVW (Ghose-Viswanadhan-Wendoloski) criteria, and development of new criteria and ranges more accurately reflecting the chemical space occupied by small molecule drugs. A relative druglikeness parameter was defined for atom types in drugs, identifying the most preferred types. The present work demonstrates that drug molecules are constitutionally more diverse relative to nondrugs, while being less diverse than leads. © 2011 American Chemical Society.


Ajmani S.,Jubilant Biosys Ltd and 96 | Viswanadhan V.N.,Jubilant Biosys Ltd and 96
Current Computer-Aided Drug Design | Year: 2013

Receptor and non-receptor tyrosine kinases have emerged as clinically useful drug target for treating certain types of cancer. It is well known that tyrosine kinase inhibitors with multi-kinases inhibitory potency are useful in anticancer therapy. In recent study, we have demonstrated application of a novel Group based QSAR (GQSAR) method to assist in lead optimization of multi-tyrosine kinase (PDGFR-beta, FGFR-1 and SRC) inhibitors. Although GQSAR method provides an alternative way to design new compounds, it could not be applied for virtual screening of large databases, because of its limitation to fragment each of the compound in the diverse database. So to circumvent this limitation of GQSAR method, herein we present the development of multi-kinase QSAR model using artificial neural networks. Various simple, easy and fast to calculate 2D/3D descriptors were used in the present analysis. The resulting neural network based QSAR (NN-QSAR) model was found to be statistically significant and provided insight into common structural requirements to inhibit different tyrosine kinases. The NN-QSAR model suggests five descriptors viz. number of rotatable bonds, number of hydrogen bond donors, number of building blocks, polar surface area and sum of nitrogen and oxygen atoms to be of major importance in explaining the activity variation in all the three kinases. In addition, this multi-target QSAR model could be useful to predict the activities of new compounds designed as tyrosine kinase inhibitors. © 2013 Bentham Science Publishers.


Mulakala C.,Jubilant Biosys Ltd and 96 | Viswanadhan V.N.,Jubilant Biosys Ltd and 96
Journal of Molecular Graphics and Modelling | Year: 2013

Implicit solvation methods such as MM-GBSA, when applied to evaluating protein/ligand binding free energies, are widely believed to be accurate only for the estimation of relative binding free energies for a congeneric series of ligands. In this work, we show that the MM-GBSA flavor of Prime 3.0, VSGB-2.0, with a variable dielectric model and a novel energy function, could be approaching the accuracy required for evaluating absolute binding free energies, albeit, through a linear regression fit. The data-set used for validation includes 106 protein-ligand complexes that were carefully selected to control for variability in the affinity data as well as error in the modeled complexes. Through systematic analysis, we also quantify the degradation in the R 2 of fit between experimental and calculated values with either greater variability in the affinity data or an increase in error in the modeled protein/ligand complexes. Limitations for its application in drug discovery are discussed along with the identification of areas for future development. © 2013 Elsevier Inc. All rights reserved.


Kristam R.,Jubilant Biosys Ltd and 96 | Kristam R.,SASTRA University | Parmar V.,Jubilant Biosys Ltd and 96 | Viswanadhan V.N.,Jubilant Biosys Ltd and 96
Journal of Molecular Graphics and Modelling | Year: 2013

TRPV1 (Transient Receptor Potential Vanilloid Type 1) receptor, a member of Transient Receptor Potential Vanilloid subfamily of ion channels, occurs in the peripheral and central nervous system, and plays a key role in transmission of pain. Consequently, this has been the target for discovery of several pain relieving agents which have undergone clinical trials. Though several TRPV1 antagonists have progressed to become clinical candidates, many are known to cause temperature elevation in humans, halting their further advancement, and signifying the need for new chemotypes. Different chemical classes of TRPV1 antagonists share three important features: an amide or an isostere flanked by an aromatic (or fused aromatic) ring with polar substitutions on one side, and a hydrophobic group on the other. Recent work identified new series of compounds with these and additional features, leading to improvement of properties, and development of clinical candidates. Herein, we describe a 3D-QSAR model (n = 62; R2 = 0.9 and Q2 = 0.75) developed from the piperazinyl-aryl series of compounds and a novel 5-point pharmacophore model is shown to fit several diverse scaffolds, six clinical candidates, five pre-clinical candidates and three lead compounds. The pharmacophore model can aid in finding new chemotypes as starting points that can be developed further. © 2013 Elsevier Inc. All rights reserved.


PubMed | Jubilant Biosys Ltd and 96
Type: Journal Article | Journal: Molecular diversity | Year: 2013

Gamma secretase (GS) is an appealing drug target for Alzheimer disease and cancer because of its central role in the processing of amyloid precursor protein and the notch family of proteins. In the absence of three-dimensional structure of GS, there is an urgent need for new methods for the prediction and screening of GS inhibitors, for facilitating discovery of novel GS inhibitors. The present study reports QSAR studies on diverse chemical classes comprising 233 compounds collected from the ChEMBL database. Herein, continuous [PLS regression and neural-network (NN)] and categorical QSAR models (NN and linear discriminant analysis) were developed to obtain pertinent descriptors responsible for variation of GS inhibitor potency. Also, SAR within various chemical classes of compounds is analyzed with respect to important QSAR descriptors, which revealed the significance of electronegative substitutions on aryl rings (PEOE3) in determining variation of GS inhibitor potency. Furthermore, substitution of acyclic amines with N-substituted cyclic amines appears to be favorable for enhancing GS inhibitor potency by increasing the values of sssN_Cnt and number of aliphatic rings. The models developed are statistically significant and improve our understanding of compounds contributing toward GS inhibitor potency and aid in the rational design of novel potent GS inhibitors.


PubMed | Jubilant Biosys Ltd and 96
Type: | Journal: Journal of molecular graphics & modelling | Year: 2013

Implicit solvation methods such as MM-GBSA, when applied to evaluating protein/ligand binding free energies, are widely believed to be accurate only for the estimation of relative binding free energies for a congeneric series of ligands. In this work, we show that the MM-GBSA flavor of Prime 3.0, VSGB-2.0, with a variable dielectric model and a novel energy function, could be approaching the accuracy required for evaluating absolute binding free energies, albeit, through a linear regression fit. The data-set used for validation includes 106 protein-ligand complexes that were carefully selected to control for variability in the affinity data as well as error in the modeled complexes. Through systematic analysis, we also quantify the degradation in the R(2) of fit between experimental and calculated values with either greater variability in the affinity data or an increase in error in the modeled protein/ligand complexes. Limitations for its application in drug discovery are discussed along with the identification of areas for future development.

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