We report the molecular design and synthesis of EG00229, 2, the first small molecule ligand for the VEGF-A receptor neuropilin 1 (NRP1) and the structural characterization of NRP1−ligand complexes by NMR spectroscopy and X-ray crystallography. Mutagenesis studies localized VEGF-A binding in the NRP1 b1 domain and a peptide fragment of VEGF-A was shown to bind at the same site by NMR, providing the basis for small molecule design. Compound 2 demonstrated inhibition of VEGF-A binding to NRP1 and attenuated VEGFR2 phosphorylation in endothelial cells. Inhibition of migration of endothelial cells was also observed. The viability of A549 lung carcinoma cells was reduced by 2, and it increased the potency of the cytotoxic agents paclitaxel and 5-fluorouracil when given in combination. These studies provide the basis for design of specific small molecule inhibitors of ligand binding to NRP1.

We show for the first time, to our knowledge, that binding of vascular endothelial growth factor (VEGF) to the neuropilin-1 b1 domain is essential for VEGF complex formation with VEGFR2/KDR (kinase insert domain-containing receptor) and is important for endothelial cell migration and tubulogenesis

Neuropilin-1 (NP-1) is a receptor for vascular endothelial growth factor-A 165 (VEGF-A

Aurora A and B kinases are closely related kinases involved in regulating separate points in the cell cycle. This review highlights the rationale for Aurora kinases as cancer targets and examines the currently known Aurora kinase inhibitors in the patent and scientific literature. The known crystal structures of the Aurora kinases are described with relevance to bound ligand interactions and the prospect of the generation of drug-resistant mutant forms. The potential for selectivity versus primary cells will also be discussed. The status of the inhibitors in clinical development is described.

Several activating mutations in the cKIT receptor tyrosine kinase are associated with the development and progression of gastrointestinal stromal tumors (GIST). Treatment of GIST with the tyrosine kinase inhibitor imatinib (Gleevec, STI571; Novartis, Basel, Switzerland) increases patient survival. However, many patients develop resistance to imatinib following initial responses. We sequenced cKIT exons from two patients with GIST after the development of imatinib resistance, revealing a point mutation in kinase domain I (exon 13), Val 654 Ala, which has been associated previously with relapse and resistance. Molecular modeling of cKIT-imatinib complexes shows that this residue is located in the drug-binding site and that the Val 654 Ala mutation disrupts drug binding by removing hydrophobic contacts with the central diaminophenyl ring of imatinib. Loss of these contacts results in a destabilizing effect on two key hydrogen bonds between imatinib and Asp 310 and Thr 670 of cKIT. Calculations based on published crystallography data show an estimated destabilization energy of 2.25 kcal/mol in the Val 654 Ala cKIT compared with wild type. When present on the same cKIT allele as an oncogenic mutation, the Val 654 Ala mutation abolishes imatinib-mediated inhibition of cKIT phosphoactivation in vitro. These results highlight some of the structural and functional consequences of the Val 654 Ala mutation in relapsing imatinib-resistant GIST and emphasize the importance of tumor genetics in drug development and patient-specific cancer treatment regimens. [Mol Cancer Ther 2005;4(12):2008 -15]

The interaction between VEGF-A and its neuropilin (NRP) receptors mediates a number of important biological effects. NRP1 and the related molecule NRP2 are widely expressed on multiple tumour types and throughout the tumour vasculature, and are emerging as critical molecules required for the progression of angiogenic diseases. Given the increasing evidence supporting a role for NRP1 in tumour development, there is growing interest in developing inhibitors of NRP1 interactions with VEGF and its other ligands. In order to probe the interaction we synthesised a number of exon 7- and 8-derived bicyclic peptides with N-terminal lipophilic groups and found a simple N-octanoyl derivative (EG00086) to be the most potent and functionally active. Detailed modelling studies indicated that new intramolecular hydrogen bonds were formed, stabilising the structure and possibly contributing to the potency. Removal of a salt bridge between D142 and R164 implicated in VEGF-A binding to neuropilin-1 had a minor effect on potency. Isothermal calorimetry was used to assess binding of EG00086 to NRP1 and NRP2, and the stability of the peptide in serum and in vivo was investigated. EG00086 is a potent blocker of VEGF-promoted cellular adhesion to extracellular matrices, and phosphorylation of p130Cas contributes to this effect.

An efficient process for the discovery of inhibitors of DDAH enzymes, without the requirement for high throughput screening, is described. Physicochemical filtering of a 308,000-compound library according to drug likeness followed by reciprocal nearest neighbour selection produced a representative subset of 35,000 compounds. Virtual screening on a dual processor PC using FlexX, followed by biological screening, identified two hit series. Similarity searches of commercial databases and chemical re-synthesis of pure compounds resulted in SR445 as an inhibitor of Pseudomonas aeruginosa DDAH at 2 microM.

Voltage-gated ion channels are a diverse family of pharmaceutically important membrane proteins for which limited 3D information is available. A number of virtual screening tools have been used to assist with the discovery of new leads and with the analysis of screening results. One such tool, and the subject of this paper, is binary kernel discrimination (BKD), a machine-learning approach that has recently been applied to applications in chemoinformatics. It uses a training set of compounds, for which both structural and qualitative activity data are known, to produce a model that can then be used to rank another set of compounds in order of likely activity. Here, we report the use of BKD to build models for the prediction of five different ion channel targets using two types of activity data. The results obtained suggest that the approach provides an effective way of prioritizing compounds for acquisition and testing.