Meurice N.,Translational Genomics Research Institute |
Wang L.,Tripos International |
Lipinski C.A.,Molecular Therapeutics |
Hulme C.,University of Arizona
Journal of Medicinal Chemistry | Year: 2010
The nonreceptor focal adhesion kinases FAK and Pyk2 play a central role in the regulation of glioma cell proliferation and migration, making them attractive targets to improve clinical outcome. Noncatalytic targeting represents a novel approach to regulate the activity of these tyrosine kinases. A combination of site directed mutagenesis and molecular modeling was used to identify compounds that target the F3 module of the Pyk2 FERM domain. A protein pharmacophore model for the Pyk2 FERM/F3 module, generated utilizing the structural conservation of ligand-bound FERM domains with known 3D structures, was used to search the LeadQuest compound library. Compounds compliant with the model were tested for their ability to inhibit the binding of a monoclonal antibody that maps to a functional site on the F3 module. The highest scoring compound bound directly to the Pyk2 FERM domain, inhibited Pyk2 stimulated glioma migration, and provides the framework for the development of novel therapeutic agents to target the activity of the focal adhesion kinases. ©2009 American Chemical Society.
Wendt B.,European Molecular Biology Laboratory EMBL |
Wendt B.,ELARA Pharmaceuticals |
Uhrig U.,Tripos International |
Bos F.,Tripos International
Journal of Chemical Information and Modeling | Year: 2011
Modeling off-target effects is one major goal of chemical biology, particularly in its applications to drug discovery. Here, we describe a new approach that allows the extraction of structure-activity relationships from large chemogenomic spaces starting from a single chemical structure. Several public source databases, offering a vast amount of data on structure and activity for a large number of different targets, have been investigated for their usefulness in automated structure-activity relationships (SAR) extraction. SAR tables were constructed by assembling similar structures around each query structure that have an activity record for a particular target. Quantitative series enrichment analysis (QSEA) was applied to these SAR tables to identify trends and to transform these trends into topomer CoMFA models. Overall more than 1700 SAR tables with topomer CoMFA models have been obtained from the ChEMBL, PubChem, and ChemBank databases. These models were able to highlight the structural trends associated with various off-target effects of marketed drugs, including cases where other structural similarity metrics would not have detected an off-target effect. These results indicate the usefulness of the QSEA approach, particularly whenever applicable with public databases, in providing a new means, beyond a simple similarity between ligand structures, to capture SAR trends and thereby contribute to success in drug discovery. © 2011 American Chemical Society.
Al-Tel T.H.,University of Sharjah |
Semreen M.H.,University of Sharjah |
Al-Qawasmeh R.A.,University of Jordan |
Schmidt M.F.,University of Cambridge |
And 5 more authors.
Journal of Medicinal Chemistry | Year: 2011
We have identified highly selective imidazopyr-idines armed with benzimidazol and/or arylimidazole as potent β-secretase inhibitors. The most effective and selective analogues demonstrated low nanomolar potency for the BACE1 enzyme as measured by FRET and cell-based (ELISA) assays and exhibited comparable affinity (K I) and high ligand efficiency (LE). In addition, these motifs were highly selective (>200) against the structurally related aspartyl protease BACE2. Our design strategy followed a traditional SAR approach and was supported by molecular modeling studies based on the previously reported hydroxyethylene transition state inhibitor derived from iso-phthalic acid I. Of the most potent compounds, 34 displayed an IC 50 for BACE1 of 18 nM and exhibited cellular activity with an EC 50 of 37 nM in the cell-based ELISA assay, as well as high affinity (K I = 17 nM) and ligand efficiency (LE = 1.7 kJ/mol). Compound 34 was found to be 204-fold more selective for BACE1 compared to the closely related aspartyl protease BACE2. (Figure presented) © 2011 American Chemical Society.