Alkynyl pyrimidines as dual EGFR/ErbB2 kinase inhibitors

EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2

The EGFR kinase pathway is one of the most frequently activated signaling pathways in human cancers. EGFR and HER2 are the two significant members of this pathway, which are attractive drug targets of clinical relevance in lung and breast cancer. Therefore, identifying EGFR- and HER2-specific inhibitors is one of the important challenges in cancer drug discovery. To address this issue, a dataset of 519 compounds having inhibitory activity against both the isoforms, i.e., EGFR and HER2, was collected from the literature and developed a knowledge-based computational classification model for predicting the specificity of a molecule for an isoform (EGFR/HER2) with precision. A total of seventy-two classification models using nine fingerprint types, four classifiers (IBK, NB, SMO and RF) and two different datasets (EGFR and HER2 isoform specific) were developed. It was observed that the models developed using random forest and IBK performed better for EGFR- and HER2-specific datasets, respectively. Scaffold and functional group analysis led to the identification of prevalent core and fragments in each of the datasets. The accuracy of the selected best performing models was also evaluated using the decoy dataset. We have also developed an application EGFRisopred, which integrates the best performing models and permits the user to predict the specificity of a compound as an EGFR-/HER2-specific anticancer agent. It is expected that the tool's availability as a free utility will allow researchers to identify new inhibitors against these targets important in cancer.

Evaluation of the published kinase inhibitor set to identify multiple inhibitors of bacterial ATP-dependent mur ligases

The Mur ligases form a series of consecutive enzymes that participate in the intracellular steps of bacterial peptidoglycan biosynthesis. They therefore represent interesting targets for antibacterial drug discovery. MurC, D, E and F are all ATP-dependent ligases. Accordingly, with the aim being to find multiple inhibitors of these enzymes, we screened a collection of ATP-competitive kinase inhibitors, on Escherichia coli MurC, D and F, and identified five promising scaffolds that inhibited at least two of these ligases. Compounds 1, 2, 4 and 5 are multiple inhibitors of the whole MurC to MurF cascade that act in the micromolar range (IC₅₀, 32-368 µM). NMR-assisted binding studies and steady-state kinetics studies performed on aza-stilbene derivative 1 showed, surprisingly, that it acts as a competitive inhibitor of MurD activity towards D-glutamic acid, and additionally, that its binding to the D-glutamic acid binding site is independent of the enzyme closure promoted by ATP.

Covalent inhibitors of EGFR family protein kinases induce degradation of human Tribbles 2 (TRIB2) pseudokinase in cancer cells

A major challenge associated with biochemical and cellular analysis of pseudokinases is a lack of target-validated small-molecule compounds with which to probe function. Tribbles 2 (TRIB2) is a cancer-associated pseudokinase with a diverse interactome, including the canonical AKT signaling module. There is substantial evidence that human TRIB2 promotes survival and drug resistance in solid tumors and blood cancers and therefore is of interest as a therapeutic target. The unusual TRIB2 pseudokinase domain contains a unique cysteine-rich C-helix and interacts with a conserved peptide motif in its own carboxyl-terminal tail, which also supports its interaction with E3 ubiquitin ligases. We found that TRIB2 is a target of previously described small-molecule protein kinase inhibitors, which were originally designed to inhibit the canonical kinase domains of epidermal growth factor receptor tyrosine kinase family members. Using a thermal shift assay, we discovered TRIB2-binding compounds within the Published Kinase Inhibitor Set (PKIS) and used a drug repurposing approach to classify compounds that either stabilized or destabilized TRIB2 in vitro. TRIB2 destabilizing agents, including the covalent drug afatinib, led to rapid TRIB2 degradation in human AML cancer cells, eliciting tractable effects on signaling and survival. Our data reveal new drug leads for the development of TRIB2-degrading compounds, which will also be invaluable for unraveling the cellular mechanisms of TRIB2-based signaling. Our study highlights that small molecule-induced protein down-regulation through drug "off-targets" might be relevant for other inhibitors that serendipitously target pseudokinases.

SAR and QSAR study on the bioactivities of human epidermal growth factor receptor-2 (HER2) inhibitors

In this paper, structure-activity relationship (SAR, classification) and quantitative structure-activity relationship (QSAR) models have been established to predict the bioactivity of human epidermal growth factor receptor-2 (HER2) inhibitors. For the SAR study, we established six SAR (or classification) models to distinguish highly and weakly active HER2 inhibitors. The dataset contained 868 HER2 inhibitors, which was split into a training set including 580 inhibitors and a test set including 288 inhibitors by a Kohonen's self-organizing map (SOM), or a random method. The SAR models were performed using support vector machine (SVM), random forest (RF) and multilayer perceptron (MLP) methods. Among the six models, SVM models obtained superior results compared with other models. The prediction accuracy of the best model (model 1A) was 90.27% and the Matthews correlation coefficient (MCC) was 0.80 on the test set. For the QSAR study, we chose 286 HER2 inhibitors to establish six quantitative prediction models using MLR, SVM and MLP methods. The correlation coefficient (r) of the best model (model 4B) was 0.92 on the test set. The descriptors analysis showed that HAccN, lone pair electronegativity and π electronegativity were closely related to the bioactivity of HER2 inhibitors.

The Discovery of Lapatinib

Protein kinases are now considered highly druggable due to the fact that there are kinase inhibitors on the market as effective drugs (e.g. Gleevec, Tarceva, and Nexavar). The class of drugs remains plagued by questions about how the inhibition profile affects the efficacy, and whether truly selective kinase inhibitors actually exist. Lapatinib remains one of the most selective kinase inhibitors in use for cancer therapy. This chapter will focus on our strategies to discover this novel, dual EGFR and ERBB-2 tyrosine kinase inhibitor using a carefully designed biological evaluation cascade coupled with a multidimensional data analysis. The primary topics discussed will cover the optimization of potency for two kinase targets, yet selective among over 500 kinases within the highly homologous ATP binding site, cellular selectivity and efficacy, and subsequent studies to understand the mechanism of action.

Computational studies of epidermal growth factor receptor: docking reliability, three-dimensional quantitative structure-activity relationship analysis, and virtual screening studies

An aberrant activity of the epidermal growth factor receptor (EGFR) has been shown to be related to many human cancers, such as breast and liver cancers, thus making EGFR an attractive target for antitumor drug discovery. In this study we evaluated the reliability of various kinds of docking software and procedures to predict the binding disposition of EGFR inhibitors. By application of the best procedure and use of more than 200 compounds, a receptor-based 3D-QSAR model for EGFR inhibition was developed. On the basis of the results obtained, the possibility of developing virtual screening studies was also evaluated. The VS procedure that proved to be the most reliable from a computational point of view was then used to filter the Maybridge database in order to identify new EGFR inhibitors. Enzymatic assays revealed that among the eight top-scoring compounds, seven proved to inhibit EGFR activity at a concentration of 100 microM, two of them exhibiting IC(50) values in the low micromolar range and one in the nanomolar range. These results demonstrate the validity of the methodologies followed. Furthermore, the two low micromolar compounds may be considered as very interesting leads for the development of new EGFR inhibitors.

Synthesis and biological evaluation of substituted 6-alkynyl-4-anilinoquinazoline derivatives as potent EGFR inhibitors

A series of C-6 or C-3' alkynyl-substituted 4-anilinoquinazoline derivatives was prepared straightforwardly by a Sonogashira reaction of the corresponding bromo-substituted 4-anilinoquinazolines. Bioactive assay of these compounds for in vitro EGFR kinase inhibition demonstrated that the novel 6-hydroxypropynyl-4-anilinoquinazoline 5e was a very potent EGFR kinase inhibitor with an IC(50) of 14 nM.