Non-thiol farnesyltransferase inhibitors: N-(4-Acylamino-3-benzoylphenyl)-4-nitrocinnamic acid amides

C-H acylation of aniline derivatives with α-oxocarboxylic acids using ruthenium catalyst

An efficient and convenient synthetic strategy for ruthenium(II)-catalyzed ortho-acylation of N-(2-pyridyl)-anilines using α-oxycarboxylic acids as acyl sources is described. The procedure can smoothly proceed under mild conditions, showing good functional group tolerance. Valuable ortho-acylated aniline products have been obtained with moderate to good yields. Furthermore, the reaction could be easily scaled up to the gram scale.

Palladium-catalyzed direct addition of arylboronic acids to 2-aminobenzonitrile derivatives: synthesis, biological evaluation and in silico analysis of 2-aminobenzophenones, 7-benzoyl-2-oxoindolines, and 7-benzoylindoles

A palladium-catalyzed direct addition of arylboronic acids to unprotected 2-aminobenzonitriles has been developed, leading to a wide range of 2-aminobenzophenones with moderate to excellent yields. The transformation has broad scope and high functional group tolerance. Moreover, 2-oxoindoline-7-carbonitrile and indole-7-carbonitrile were applicable to this process for the construction of 7-benzoyl-2-oxoindolines and 7-benzoylindoles, respectively. Among the compounds examined, compound 4e possessed the most potent anticancer activity against H446 and HGC-27 in vitro, with IC50 values of 0.02 μmol L(-1) and 0.09 μmol L(-1), respectively, while compound 4a showed the best potent anticancer activity against SGC-7901 with an IC50 value of 0.01 μmol L(-1). Furthermore, we also performed in silico molecular docking calculations to investigate the interaction mode and binding affinity between the examined compounds and their tubulin target.

Three-dimensional quantitative structure-farnesyltransferase inhibition analysis for some diaminobenzophenones

A 3D-QSAR investigation of 95 diaminobenzophenone yeast farnesyltransferase (FT) inhibitors selected from the work of Schlitzer et al. showed that steric, electrostatic, and hydrophobic properties play key roles in the bioactivity of the series. A CoMFA/CoMSIA combined model using the steric and electrostatic fields of CoMFA together with the hydrophobic field of CoMSIA showed significant improvement in prediction compared with the CoMFA steric and electrostatic fields model. The similarity of the 3D-QSAR field maps for yeast FT inhibition activity (from this work) and for antimalarial activity data (from previous work) and the correlation between those activities are discussed.

Modeling of farnesyltransferase inhibition by some thiol and non-thiol peptidomimetic inhibitors using genetic neural networks and RDF approaches

Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors. A linear model was unable to successfully fit the whole data set; however, the optimum Bayesian regularized neural network model described about 87% inhibitory activity variance with a relevant predictive power measured by q2 values of leave-one-out and leave-group-out cross-validations of about 0.7. According to their activity levels, thiol and non-thiol inhibitors were well-distributed in a topological map, built with the inputs of the optimum non-linear predictor. Furthermore, descriptors in the GNN model suggested the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.

Broad-Based Quantitative Structure−Activity Relationship Modeling of Potency and Selectivity of Farnesyltransferase Inhibitors Using a Bayesian Regularized Neural Network

Inhibitors of the enzyme farnesyltransferase show potential as novel anticancer agents. There are many known inhibitors, but efforts to build predictive SAR models have been hampered by the structural diversity and flexibility of inhibitors. We have undertaken for the first time a QSAR study of the potency and selectivity of a large, diverse data set of farnesyltransferase inhibitors. We used novel molecular descriptors based on binned atomic properties and invariants of molecular matrices and a robust, nonlinear QSAR mapping paradigm, the Bayesian regularized neural network. We have built robust QSAR models of farnesyltransferase inhibition, geranylgeranyltransferase inhibition, and in vivo data. We have derived a novel selectivity index that allows us to model potency and selectivity simultaneously and have built robust QSAR models using this index that have the potential to discover new potent and selective inhibitors.