2-(2-Methyl-2-nitrovinyl)furan but Not Furvina Interfere with Staphylococcus aureus Agr Quorum-Sensing System and Potentiate the Action of Fusidic Acid against Biofilms.

Quorum sensing (QS) plays an essential role in the production of virulence factors, in biofilm formation and antimicrobial resistance. Consequently, inhibiting QS is being considered a promising target for antipathogenic/anti-virulence therapies. This study aims to screen 2-nitrovinylfuran derivatives structurally related to Furvina (a broad-spectrum antibiotic already used for therapeutic purposes) for their effects on QS and in biofilm prevention/control. Furvina and four 2-nitrovinylfuran derivatives (compounds 1-4) were tested to assess the ability to interfere with QS of Staphylococcus aureus using bioreporter strains (S. aureus ALC1742 and ALC1743). The activity of Furvina and the most promising quorum-sensing inhibitor (QSI) was evaluated in biofilm prevention and in biofilm control (combined with fusidic acid). The biofilms were further characterized in terms of biofilm mass, viability and membrane integrity. Compound 2 caused the most significant QS inhibition with reductions between 60% and 80%. Molecular docking simulations indicate that this compound interacts preferentially with the protein hydrophobic cleft in the LytTR domain of AgrA pocket. Metabolic inactivations of 40% for S. aureus ALC1742 and 20% for S. aureus ALC1743 were reached. A 24 h-old biofilm formed in the presence of the QSI increased the metabolic inactivation by fusidic acid to 80%, for both strains. The overall results highlight the effects of compound 2 as well as the potential of combining QSI with in-use antibiotics for the management of skin and soft tissues infections.

Desirability-based methods of multiobjective optimization and ranking for global QSAR studies. Filtering safe and potent drug candidates from combinatorial libraries.

Up to now, very few applications of multiobjective optimization (MOOP) techniques to quantitative structure-activity relationship (QSAR) studies have been reported in the literature. However, none of them report the optimization of objectives related directly to the final pharmaceutical profile of a drug. In this paper, a MOOP method based on Derringer's desirability function that allows conducting global QSAR studies, simultaneously considering the potency, bioavailability, and safety of a set of drug candidates, is introduced. The results of the desirability-based MOOP (the levels of the predictor variables concurrently producing the best possible compromise between the properties determining an optimal drug candidate) are used for the implementation of a ranking method that is also based on the application of desirability functions. This method allows ranking drug candidates with unknown pharmaceutical properties from combinatorial libraries according to the degree of similarity with the previously determined optimal candidate. Application of this method will make it possible to filter the most promising drug candidates of a library (the best-ranked candidates), which should have the best pharmaceutical profile (the best compromise between potency, safety and bioavailability). In addition, a validation method of the ranking process, as well as a quantitative measure of the quality of a ranking, the ranking quality index (Psi), is proposed. The usefulness of the desirability-based methods of MOOP and ranking is demonstrated by its application to a library of 95 fluoroquinolones, reporting their gram-negative antibacterial activity and mammalian cell cytotoxicity. Finally, the combined use of the desirability-based methods of MOOP and ranking proposed here seems to be a valuable tool for rational drug discovery and development.

A topological substructural approach applied to the computational prediction of rodent carcinogenicity.

The carcinogenic activity has been investigated by using a topological substructural molecular design approach (TOPS-MODE). A discriminant model was developed to predict the carcinogenic and noncarcinogenic activity on a data set of 189 compounds. The percentage of correct classification was 76.32%. The predictive power of the model was validated by three test: an external test set (compounds not used in the develop of the model, with a 72.97% of good classification), a leave-group-out cross-validation procedure (4-fold full cross-validation, removing 20% of compounds in each cycle, with a good prediction of 76.31%) and two external prediction sets (the first and second exercises of the National Toxicology Program). This methodology evidenced that the hydrophobicity increase the carcinogenic activity and the dipole moment of the molecule decrease it; suggesting the capacity of the TOPS-MODE descriptors to estimate this property for new drug candidates. Finally, the positive and negative fragment contributions to the carcinogenic activity were identified (structural alerts) and their potentialities in the lead generation process and in the design of 'safer' chemicals were evaluated.

A novel approach to predict a toxicological property of aromatic compounds in the Tetrahymena pyriformis.

The TOPological Substructural MOlecular DEsign (TOPS-MODE) has been successfully used in order to explain the toxicity in the Tetrahymena pyriformis on a large data set. The obtained models for the training set had good statistical parameters (R(2)=0.72-0.81, p<0.05) an also the prediction power of the models found was adequate (Q(2)=0.70-0.80). A detailed study of the influence of variable numbers in the equation and the statistical outliers was carried out; leading to a good final model with a better physicochemical interpretation than the rest of the published models. Only two molecular descriptors codifying dipolar and hydrophobic features were introduced. Finally, the fragment contributions to the toxicity prediction evidenced the powerful of this topological approach.