Current Topics in Medicinal Chemistry - Volume 12, Issue 24, 2012

Despite significant research in understanding of neoplastic diseases, the success rate for oncology drugs is relatively very low. A major challenge before the scientific community is to design new chemical entities that will be highly selective for cancer cells so as to minimize side effects. Classification models (CMs) models play a prominent role in prediction of the biological properties of newly designed compounds before their synthesis and prevent non-optimal use of resources. Though correlation models far outnumber classification models for development of various therapeutic agents but the significance of classification models for development of anti-cancer agents can not be underestimated. Various techniques employed for development of classification models for anti-cancer activity have been briefly reviewed. Moreover, successful use of some of these classification techniques for the development of models for anti-proliferative activity has been illustrated using a data set comprising of 53 analogues of N-Benzoylated phenoxazines and phenothiazines. Resulting classification models with high degree of accuracy can play a vital role in providing lead structures for the development of novel anti-proliferative agents for cancer chemotherapy.

The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a Kd of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: Kd = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.

A modified version of the CORAL software (http://www.insilico.eu/coral) allows building up the classification model for the case of the Yes/No data on the anti-sarcoma activity of organic compounds. Three random splits into the sub-training, calibration, and test sets of the data for 3017 compounds were examined. The performance of the proposed approach is satisfactory. The average values of the statistical characteristics for external test set on three random splits are as follows: n=1173-1234, sensitivity = 0.8903±0.0390, specificity = 0.9869±0.0013, and accuracy = 0.9759±0.0043. Mechanistic interpretation of the suggested model is discussed.

The discovery of anti-cancer agents is an area which continues in accelerated expansion. Leukemias (Lkms) are among the most investigated cancers due to its high and dominant prevalence in children. Computer-aided drug design (CADD) methodologies have been extremely important for the discovery of potent anti-Lkms agents, providing essential insights about the molecular patterns which could be involved in the appearance and development of anti-Lkms activity. The present review is focused on the role of the current CADD methodologies for the discovery of anti-Lkms agents with strong emphasis on the in silico prediction of inhibitors against the primary protein associated with the appearance of Lkms: Abelson tyrosine-protein kinase 1 (TPK-ABL1). In order to make a contribution to the field, we also developed a unified ligand-based approach by exploring Quantitative-Structure Activity Relationships (QSAR) studies. Here, we focused on the construction of two multi-targets (mt) QSAR models by employing a large and heterogeneous database of compounds. These models exhibited excellent statistical quality and predictive power to classifying more than 92% of inhibitors/ no inhibitors against seven proteins associated with Lkms, in both training and prediction sets. By using our unified ligand-based approach we identified several fragments as responsible for the anti-Lkms activity through inhibition of proteins, and new molecules were suggested as versatile inhibitors of the seven proteins under study.

4-Thiazolidinone is a promising scaffold for the search of new potential antibacterial, antiviral, antidiabetic and anticancer agents etc. SAR analysis of the most potent compounds and different activities evaluation provide a solid background for de novo design of novel drugs. Current review summarizes recent QSAR studies on the 4-thiazolidinones making the emphasis on both technical and interpretative sides of reported models. Several papers among them are devoted to the anticancer activity of 4-thiazolidinone derivatives and are reporting QSAR models that were obtained via multiple linear regressions (MLR). Additionally, a non-linear approach, namely Gaussian processes, has been applied to identify the relationships between 4-thiazolidinones structure and tumor cell growth inhibition. The interpretation of the reported model highlights the core template for the design of new highly-potent anticancer agents and proposes a hypothesis about key role of Hydrogen at the N-atom three bonds away from thiazolidine in the interaction with biotarget.

Flavonoids are phenolic compounds, secondary metabolites of plants that cause several benefits to our health, including helping the treatment against cancer. These pharmacological properties are associated with the ability of flavonoids in attenuating the generation of reactive oxygen species, acting as chelate compounds or affecting the oxi-redox cycle. In spite of the large number of information in term of SAR and QSAR, no recent review has tabulated and discussed in detail these data. In view of this, we bring here a detailed discussion of the structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) models. We have also analyzed the correlation between the chemical structure of flavonoids and analogues to their anticancer activities. A large number of methodologies have been used to identify the characteristics of these compounds with their potential anticancer: multiple linear regression, principal components analysis, comparative molecular field analysis, comparative molecular similarity indices analysis, partial least squares, neural networks, configuration of classification and regression trees, Free-Wilson, docking; using topological, structural and enthalpies' descriptors. We also discussed the use of docking models, together with QSAR models, for the virtual screening of anticancer flavonoids. The importance of docking models to the medicinal chemistry of anticancer flavonoids has increased in the last decade, especially to help in identifying the structural determinants responsible for the activity. We tabulated here the most important examples of virtual screening determined for anticancer flavonoids and we highlighted the structural determinants. The mode of action, the most potent anticancer flavonoids and hints for the structural design of anticancer flavonoids are revised in details and provided here.

Three new triphenyltin(IV) complexes, viz., triphenylstannyl 2-((E)-(4-hydroxy-3-((E)-((4-(methoxycarbonyl) phenyl)imino)methyl)phenyl)-diazenyl)benzoate (Ph3SnL2H: 2), methyl 2-((E)-(4-hydroxy-3-((E)-((4- (((triphenylstannyl)oxy)carbonyl)phenyl)imino)methyl)phenyl)diazenyl)benzoate (Ph3SnL3H: 3), and triphenylstannyl 2- ((E)-(4-hydroxy-3-((E)-((4-(((triphenylstannyl)oxy)carbonyl)phenyl)imino)methyl)phenyl)diazenyl)benzoate ((Ph3Sn)2 L4H: 4) were synthesized and characterized by spectroscopic (1H, 119Sn NMR and IR) techniques in combination with elemental analysis. The 119Sn NMR spectral data were recorded in a non-coordinating solvent and indicate tetrahedral coordination geometry in solution. In the solid state, a single-crystal X-ray diffraction analysis of the dinuclear complex (Ph3Sn)2L4H (4) revealed a monocapped tetrahedral coordination geometry with anisobidentate coordination modes of the carboxylate groups with average bond angles around the Sn atoms of 113.5 and 112.2°, respectively. In vitro cytotoxicity studies were performed with all three complexes 2-4, along with a previously reported parent aquatriphenylstannyl complex, 2-((3-formyl-4-hydroxyphenyl)diazenyl)benzoate (Ph3SnL1H.OH2 (1)) across a panel of human tumor cell lines, viz., A498, EVSA-T, H226, IGROV, M19 MEL, MCF-7 and WIDR. The screening results were compared with those from related triphenyltin(IV) carboxylates containing (i) imino (11-16) and (ii) diazenyl frameworks (1, 5-10). In general, complexes 2-4 exhibited good cytotoxic activity and among them, compound 4 was found to be the best performer, particularly for EVSA-T and MCF-7 cell lines. Additionally, 4 scored better activity than cisplatin (2-15 folds), 5-fluorouracil and etoposide across a panel of cell lines. Docking studies indicated that the diazenyl and imino nitrogen atoms, and the oxygen atoms of triphenyltin ester, methyl ester and phenolic group play an important role for the complexation of the organotin compounds in the active sites of enzymes such as ribonucleotide reductase (pdb ID: 4R1R), thymidylate synthase (pdb ID: 2G8D), thymidylate phosphorylase (pdb ID: 1BRW) and topoisomerase II (pdb ID: 1QZR).

This paper compiled all the previous reports on radical scavengers, an interesting class of ribonucleotide reductase inhibitors. We have highlighted three key research areas: chemical classification of radical scavengers, structural and functional aspects of the radical site, and progress in drug designing for radical scavengers. Under the chemical classification section, we have recorded the discovery of hydroxyurea followed by discussions on hydroxamic acids, amidoximes, hydroxyguanidines, and phenolic compounds. In the next section, we have compiled the structural information for the radical site obtained from different crystallographic and theoretical studies. Finally, we have included the reported ligand based and structure based drug-designing studies.

Guanine-rich sequences found at telomeres and oncogenes have the capacity to form G-quadruplex (G4) structures. It has been found a relationship between the ability to stabilizing G4 structures and anticancer activity. Guanine quadruplexes stabilization and its implication in cancer phenomena is a therapeutic target relatively recent. Computeraided drug design has been a very useful tool for the search of new candidates. In last years, methodologies have improved with the development of the computational sciences. The hardware is also enhanced, new techniques are explored. NMR and X-ray information about different targets are discovered continually. The continuous augmentation of new powerful and comprehensive software's with this purpose is other significant factor that contributes to the discovering of new compounds. Nevertheless computer-aided drug design has not been vastly employed in the design of new compound with G4 stabilization activity. All things considered, this review will be focused on the influence of computational techniques on speeding up the discovery of new G4 ligands.