Non-thiol farnesyltransferase inhibitors: N-(4-acylamino-3-benzoylphenyl)-3-[5-(4-nitrophenyl)-2-furyl]acrylic acid amides

Rapid and Efficient Microwave-Assisted Friedländer Quinoline Synthesis

A microwave-based methodology facilitates reaction of 2-aminophenylketones with cyclic ketones to form a quinoline scaffold. Syntheses of amido- and amino-linked 17β-hydroxysteroid dehydrogenase type 3 inhibitors with a benzophenone-linked motif were pursued using 2-aminobenzophenone as building block. Two amido-linked targets were achieved in modest yield, but when using microwave-assisted reductive amination for the amino-linked counterparts an unexpected product was observed. X-ray crystallography revealed it as a quinoline derivative, leading to optimisation of a simple and efficient modification of Friedländer methodology. Using reagents and acetic acid catalyst in organic solvent the unassisted reaction proceeds only over several days and in very poor yield. However, by employing neat acetic acid as both solvent and acid catalyst with microwave irradiation at 160 °C quinoline synthesis is achieved in 5 minutes in excellent yield. This has advantages over the previously reported high temperatures or strong acids required, not least given the green credentials of acetic acid, and examples using diverse ketones illustrate applicability. Additionally, he unassisted reaction proceeds effectively at room temperature, albeit much more slowly.

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.

3D-QSAR analysis of antimalarial farnesyltransferase inhibitors based on a 2,5-diaminobenzophenone scaffold

With annual death tolls in the millions and emerging resistance to existing drugs, novel therapies are needed against malaria. Wiesner et al. recently developed a novel class of antimalarials derived from farnesyltransferase inhibitors based on a 2,5-diaminobenzophenone scaffold. The compounds displayed a wide range of activity, including submicromolar, against the multi-drug resistant Plasmodium falciparum strain Dd2. In order to investigate quantitatively the local physicochemical properties involved in the interaction between drug and biotarget, we used the 3D-QSAR methods CoMFA and CoMSIA to study some of the series, including the screened lead compound 2,5-bis-acylaminobenzophenone, 28 cinnamic acid derivatives, 29 N-(3-benzoyl-4-tolylacetylaminophenyl)-3-(5-aryl-2-furyl)acrylic acid amides, and 34 N-(4-substituted-amino-3-benzoylphenyl)-[5-(4-nitrophenyl)-2-furyl]acrylic acid amides. We found that steric, electrostatic, and hydrophobic properties of substituent groups play key roles in the bioactivity of the series of compounds, while hydrogen bonding interactions show no obvious impact. We built several highly predictive 3D-QSAR models, including a CoMSIA one composed of steric, electrostatic, and hydrophobic fields, with r(2)=0.94, q(2)=0.63, and r(pred)(2)=0.63. The results provide insight for optimization of this class of antimalarials for better activity and may prove helpful for further lead optimization.

Benzophenone-based farnesyltransferase inhibitors with high activity against Trypanosoma cruzi

Less toxic drugs are needed to combat the human parasite Trypanosoma cruzi (Chagas's disease). One novel target for antitrypanosomal drug design is farnesyltransferase. Several farnesyltransferase inhibitors based on the benzophenone scaffold were assayed in vitro and in vivo with the parasite. The common structural feature of all inhibitors is an amino function which can be protonated. Best in vitro activity (LC50 values 1 and 10 nM, respectively) was recorded for the R-phenylalanine derivative 4a and for the N-propylpiperazinyl derivative 2f. These inhibitors showed no cytotoxicity to cells. When tested in vivo, the survival rates of infected animals receiving the inhibitors at 7 mg/kg body weight/day were 80 and 60% at day 115 postinfection, respectively.

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.

Non-thiol farnesyltransferase inhibitors: N-(4-aminoacylamino-3-benzoylphenyl)-3-[5-(4-nitrophenyl)-2 furyl]acrylic acid amides and their antimalarial activity

Water solubility was previously found to be essential for in vivo-antimalarial activity of a novel type of benzophenone-based farnesyltransferase inhibitors. Introduction of a alpha-amino group into the phenylacetic acid substructure provided more soluble compounds with high farnesyltransferase inhibitory activity. The in vitro-antimalarial activity was detrimentally influenced by this structural modification.

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.

Non-thiol farnesyltransferase inhibitors: N-(4-tolylacetylamino-3-benzoylphenyl)-3-arylfurylacrylic acid amides

We have designed arylfurylacryl-substituted benzophenones as non-thiol farnesyltransferase inhibitors utilizing a novel aryl binding site of farnesyltransferase. These compounds display activity in the low nanomolar range.