Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands.

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 µM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

FDA-approved drugs selected using virtual screening bind specifically to G-quadruplex DNA.

Guanine-rich sequences found in telomeres and oncogene promoters have the ability to form G-quadruplex structures. In this paper we describe the use of a virtual screening assay to search a database of FDA-approved compounds for compounds with the potential to bind G-quadruplex DNA. More than 750 telomerase inhibitors were identified in a literature search as acting through G-quadruplex stabilization, and from evaluation of these compounds, theoretical models capable of discriminating new compounds that bind G-quadruplex DNA were developed. Six compounds predicted to bind to the G-quadruplex structure were tested for their ability to bind to the human telomeric DNA sequence. Prochloroperazine, promazine, and chlorpromazine stabilized the G-quadruplex structure as determined by fluorescence resonance energy transfer techniques. These compounds also bound to promoter sequences of oncogenes such as c-myc and K-ras. Amitriptyline, imipramine, and loxapine were less stabilizing but did bind to the G-quadruplex. The ability of prochloroperazine, promazine, and chlorpromazine to recognize G-quadruplex structures was confirmed using a fluorescent intercalator displacement assay, in which displacement of thiazole orange from G-quadruplex structures was demonstrated. Interestingly, these compounds exhibited selectivity for the G-quadruplex structure as all had poor affinity for the duplex sequence.

Computational tools in the discovery of new G-quadruplex ligands with potential anticancer activity.

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. Computer-aided 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.

Prediction of telomerase inhibitory activity for acridinic derivatives based on chemical structure.

Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.