Nishi-Tokyo-shi, Japan
Nishi-Tokyo-shi, Japan

Time filter

Source Type

Patent
PharmaDesign Inc. and NB Health Laboratory Co. | Date: 2013-06-19

There is provided a novel oxazolone derivative having inhibitory activity against casein kinase 1 and casein kinase 1. In addition, the present inhibitor inhibits casein kinase 1 and casein kinase 1, and thus there is also provided a pharmaceutical agent useful for the treatment and/or prevention of diseases, with the pathological conditions of which the activation mechanism of casein kinase 1 or casein kinase 1 is associated. There is further provided a pharmaceutical agent useful for the treatment of, particularly, circadian rhythm disorder (including sleep disorder), central neurodegenerative disease, and cancer. An inhibitor of casein kinase 1 and casein kinase 1 comprising, as an active ingredient, an oxazolone derivative represented by the following general formula (1), a salt thereof, a solvate thereof, or a hydrate thereof:


Patent
PharmaDesign Inc. and NB Health Laboratory Co. | Date: 2011-12-21

There is provided an inhibitor that inhibits casein kinase 1 and casein kinase 1, and thus, there is also provided a pharmaceutical agent useful for the treatment and/or prevention of a disease, with the pathological condition of which the mechanism of activation of casein kinase 1 or casein kinase 1 is associated. Particularly, the above-described inhibitor is used to provide a pharmaceutical agent useful for the treatment of circadian rhythm disorder (including sleep disorder), central neurodegenerative disease, and cancer. An inhibitor of casein kinase 1 and casein kinase 1, which comprises, as an active ingredient, an oxazolone derivative represented by the following general formula (1), a salt thereof, a solvate thereof, or a hydrate thereof:_(1) and R_(2) independently represents any one of a substituted or unsubstituted 6-membered or 5-membered heterocyclic group optionally having a condensed ring, a substituted or unsubstituted aromatic hydrocarbon group optionally having a condensed ring, and a substituted or unsubstituted aromatic hydrocarbon lower alkyl group or aromatic hydrocarbon lower alkenyl group optionally having a condensed ring.]


Patent
Nb Health Laboratory Co. and PharmaDesign Inc. | Date: 2011-08-08

There is provided a novel oxazolone derivative having inhibitory activity against casein kinase 1 and casein kinase 1. In addition, the present inhibitor inhibits casein kinase 1 and casein kinase 1, and thus there is also provided a pharmaceutical agent useful for the treatment and/or prevention of diseases, with the pathological conditions of which the activation mechanism of casein kinase 1 or casein kinase 1 is associated. There is further provided a pharmaceutical agent useful for the treatment of particularly, circadian rhythm disorder (including sleep disorder), central neurodegenerative disease, and cancer. An inhibitor of casein kinase 1 and casein kinase 1 comprising, as an act ingredient, an oxazolone derivative represented by the following general formula (1), a salt thereof, a solvate thereof, or a hydrate thereof: wherein X represents a halogen atom (which may be any one of a fluorine atom, a chlorine atom, a bromine atom, and an iodine atom).


Nikaido Y.,Gunma University | Koyama Y.,Gunma University | Yoshikawa Y.,PharmaDesign Inc. | Furuya T.,PharmaDesign Inc. | Takeda S.,Gunma University
Journal of Biochemistry | Year: 2015

GPR84 is a G protein-coupled receptor for medium-chain fatty acids. Capric acid and 3,3′-diindolylmethane are specific agonists for GPR84. We built a homology model of a GPR84-capric acid complex to investigate the ligand-binding mode using the crystal structure of human active-state β2-adrenergic receptor. We performed site-directed mutagenesis to subject ligand-binding sites to our model using GPR84-Giα fusion proteins and a [35S]GTPγS-binding assay. We compared the activity of the wild type and mutated forms of GPR84 by [35S]GTPγS binding to capric acid and diindolylmethane. The mutations L100D 'Ballesteros-Weinstein numbering: 3.32), F101Y (3.33) and N104Q (3.36) in the transmembrane helix III and N357D (7.39) in the transmembrane helix VII resulted in reduced capric acid activity but maintained the diindolylmethane responses. Y186F (5.46) and Y186H (5.46) mutations had no characteristic effect on capric acid but with diindolylmethane they significantly affected the G protein activation efficiency. The L100D (3.32) mutant responded to decylamine, a fatty amine, instead of a natural agonist, the fatty acid capric acid, suggesting that we have identified a mutated G protein-coupled receptor-artificial ligand pairing. Our molecular model provides an explanation for these results and interactions between GPR84 and capric acid. Further, from the results of a double stimulation assay, we concluded that diindolylmethane was a positive allosteric modulator for GPR84. © 2014 The Authors 2014. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.


Yoshikawa Y.,PharmaDesign Inc. | Kobayashi K.,Kyoto University | Oishi S.,Kyoto University | Fujii N.,Kyoto University | And 2 more authors.
Bioorganic and Medicinal Chemistry Letters | Year: 2012

CXCR4 is a G-protein coupled receptor that is associated with many diseases such as breast cancer metastasis, HIV infection, leukemic disease and rheumatoid arthritis, and is thus considered an attractive drug target. Previously, we identified a cyclic pentapeptide, FC131, that is a potent antagonist for CXCR4. In this study, we constructed a three dimensional model of the CXCR4-FC131 complex. To investigate the backbone flexibility of FC131, we performed molecular dynamics simulations of FC131 based on the NMR structure of FC131, and obtained snapshot structures from the trajectories which were used to model the docking pose of FC131 into CXCR4. Our final model of the CXCR4-FC131 complex is partially different from the X-ray crystal structure of CXCR4-CVX15 and suggests water-mediated interactions. Nevertheless, this docking pose is consistent with the experimental data. We believe our model will aid in the discovery and development of small-molecule antagonists for CXCR4. © 2011 Elsevier Ltd. All rights reserved.


Yoshikawa Y.,PharmaDesign Inc. | Oishi S.,Kyoto University | Kubo T.,Kyoto University | Tanahara N.,Kyoto University | And 2 more authors.
Journal of Medicinal Chemistry | Year: 2013

Homology modeling of G-protein-coupled seven-transmembrane receptors (GPCRs) remains a challenge despite the increasing number of released GPCR crystal structures. This challenge can be attributed to the low sequence identity and structural diversity of the ligand-binding pocket of GPCRs. We have developed an optimized GPCR structure modeling method based on multiple GPCR crystal structures. This method was designed to be applicable to distantly related receptors of known structural templates. CXC chemokine receptor (CXCR7) is a potential drug target for cancer chemotherapy. Homology modeling, docking, and virtual screening for CXCR7 were carried out using our method. The predicted docking poses of the known antagonists were different from the crystal structure of human CXCR4 with the small-molecule antagonist IT1t. Furthermore, 21 novel CXCR7 ligands with IC50 values of 1.29-11.4 μM with various scaffolds were identified by structure-based virtual screening. © 2013 American Chemical Society.


Sugaya N.,PharmaDesign Inc.
Database : the journal of biological databases and curation | Year: 2012

Druggable Protein-protein Interaction Assessment System (Dr. PIAS) is a database of druggable protein-protein interactions (PPIs) predicted by our support vector machine (SVM)-based method. Since the first publication of this database, Dr. PIAS has been updated to version 2.0. PPI data have been increased considerably, from 71,500 to 83,324 entries. As the new positive instances in our method, 4 PPIs and 10 tertiary structures have been added. This addition increases the prediction accuracy of our SVM classifier in comparison with the previous classifier, despite the number of added PPIs and structures is small. We have introduced the novel concept of 'similar positives' of druggable PPIs, which will help researchers discover small compounds that can inhibit predicted druggable PPIs. Dr. PIAS will aid the effective search for druggable PPIs from a mine of interactome data being rapidly accumulated. Dr. PIAS 2.0 is available at http://www.drpias.net.


Takahashi T.,Kobe University | Shibasaki T.,Kobe University | Takahashi H.,Kobe University | Sugawara K.,Kobe University | And 4 more authors.
Science Signaling | Year: 2013

Sulfonylureas are widely used drugs for treating insulin deficiency in patients with type 2 diabetes. Sulfonylureas bind to the regulatory subunit of the pancreatic β cell potassium channel that controls insulin secretion. Sulfonylureas also bind to and activate Epac2A, a member of the Epac family of cyclic adenosine monophosphate (cAMP) -binding proteins that promote insulin secretion through activation of the Ras-like guanosine triphosphatase Rap1. Using molecular docking simulation, we identified amino acid residues in one of two cyclic nucleotide - binding domains, cNBD-A, in Epac2A predicted to mediate the interaction with sulfonylureas. We confirmed the importance of the identified residues by site-directed mutagenesis and analysis of the response of the mutants to sulfonylureas using two assays: changes in fluorescence resonance energy transfer (FRET) of an Epac2A-FRET biosensor and direct sulfonylurea-binding experiments. These residues were also required for the sulfonylureadependent Rap1 activation by Epac2A. Binding of sulfonylureas to Epac2A depended on the concentration of cAMP and the structures of the drugs. Sulfonylureas and cAMP cooperatively activated Epac2A through binding to cNBD-A and cNBD-B, respectively. Our data suggest that sulfonylureas stabilize Epac2A in its open, active state and provide insight for the development of drugs that target Epac2A.


Machine learning methods based on ligand-protein interaction data in bioactivity databases are one of the current strategies for efficiently finding novel lead compounds as the first step in the drug discovery process. Although previous machine learning studies have succeeded in predicting novel ligand-protein interactions with high performance, all of the previous studies to date have been heavily dependent on the simple use of raw bioactivity data of ligand potencies measured by IC50, EC50, Ki, and Kd deposited in databases. ChEMBL provides us with a unique opportunity to investigate whether a machine-learning-based classifier created by reflecting ligand efficiency other than the IC50, EC50, Ki, and Kd values can also offer high predictive performance. Here we report that classifiers created from training data based on ligand efficiency show higher performance than those from data based on IC 50 or Ki values. Utilizing GPCRSARfari and KinaseSARfari databases in ChEMBL, we created IC50- or Ki-based training data and binding efficiency index (BEI) based training data then constructed classifiers using support vector machines (SVMs). The SVM classifiers from the BEI-based training data showed slightly higher area under curve (AUC), accuracy, sensitivity, and specificity in the cross-validation tests. Application of the classifiers to the validation data demonstrated that the AUCs and specificities of the BEI-based classifiers dramatically increased in comparison with the IC50- or Ki-based classifiers. The improvement of the predictive power by the BEI-based classifiers can be attributed to (i) the more separated distributions of positives and negatives, (ii) the higher diversity of negatives in the BEI-based training data in a feature space of SVMs, and (iii) a more balanced number of positives and negatives in the BEI-based training data. These results strongly suggest that training data based on ligand efficiency as well as data based on classical IC50, EC50, Kd, and Ki values are important when creating a classifier using a machine learning approach based on bioactivity data. © 2013 American Chemical Society.


Sugaya N.,PharmaDesign Inc. | Furuya T.,PharmaDesign Inc.
BMC Bioinformatics | Year: 2011

Background: The amount of data on protein-protein interactions (PPIs) available in public databases and in the literature has rapidly expanded in recent years. PPI data can provide useful information for researchers in pharmacology and medicine as well as those in interactome studies. There is urgent need for a novel methodology or software allowing the efficient utilization of PPI data in pharmacology and medicine.Results: To address this need, we have developed the 'Druggable Protein-protein Interaction Assessment System' (Dr. PIAS). Dr. PIAS has a meta-database that stores various types of information (tertiary structures, drugs/chemicals, and biological functions associated with PPIs) retrieved from public sources. By integrating this information, Dr. PIAS assesses whether a PPI is druggable as a target for small chemical ligands by using a supervised machine-learning method, support vector machine (SVM). Dr. PIAS holds not only known druggable PPIs but also all PPIs of human, mouse, rat, and human immunodeficiency virus (HIV) proteins identified to date.Conclusions: The design concept of Dr. PIAS is distinct from other published PPI databases in that it focuses on selecting the PPIs most likely to make good drug targets, rather than merely collecting PPI data. © 2011 Sugaya and Furuya; licensee BioMed Central Ltd.

Loading PharmaDesign Inc. collaborators
Loading PharmaDesign Inc. collaborators