Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure–activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.Ciencias AmbientalesCiencias de la AlimentaciónFarmaciaIngeniería, Industria y ConstrucciónMedicin

Furvina (G-1) in vitro effectiveness on microorganisms isolated from skin infections in dogs

Los objetivos del presente estudio consistieron en aislar microorganismos de la piel de caninos con piodermitis de la ciudad de Santa Clara, Cuba, y evaluar la actividad in vitro de la furvina frente a los microorganismos aislados. La resistencia a los agentes antibacterianos se determinó mediante el método de difusión en agar y la actividad in vitro se evaluó cuantitativamente mediante macrodilución en caldo según procedimientos internacionales. Se identificaron 50 cepas multirresistentes, siendo 47 del género Staphylococcus y 3 del género Pseudomonas. El test exacto de Fisher demostró sensibilidad altamente significativa de los microorganismos del género Staphylococcus para la ciprofloxacina. Todas las cepas fueron resistentes al menos a tres de los antibacterianos enfrentados. Las muestras evaluadas mostraron una Concentración Inhibitoria Mínima (CIM) entre 2-16 g/ml frente a furvina, destacándose que el 83.2% de las cepas osciló entre 4 y 8 g/ml, y una Concentración Bactericida Mínima (CBM) entre 4-32 g/ml, resaltando que el 86.2% de los aislados osciló entre 8 y 16 g/ml. Todas las cepas evaluadas mostraron una CIM50 frente a furvina de 5.59 g/ml (4.70-6.48) y una CBM50 de 11.8 g/ml (10.0-13.6). Los resultados permiten inferir que furvina posee una actividad bactericida in vitro frente a las cepas sensibles y resistentes aisladas de lesiones en la piel de caninos.The aim of this study was to evaluate the in vitro activity of furvina against microorganisms isolated from the skin of canines with piodermatitis in of Santa Clara, Cuba. Resistance to antibacterial agents was assessed by the diffusion method in agar and the in vitro activity was evaluated quantitatively by the broth macrodilution method according to international clinical laboratory standards. Fifty multiresistant strains were identified, 47 from the genus Staphylococcus and 3 from Pseudomonas. The Fisher’s exact test showed highly significant sensibility of microorganisms of the genus Staphylococcus to ciprofloxacin. All strains were resistant to at least three antimicrobials. The Minimum Inhibitory Concentration (MIC) for furvina ranged from 2-16 mg/ml where 83.2% of the strains having a MIC of 4-8 mg/mL. The Minimum Bactericidal Concentration (MBC) ranged from 4-32 mg/ml where 86.2% of the isolates oscillating between 8 and 16 mg/ml. All strains showed a MIC50 against furvina of 5.59 μg/ml (4.70-6.48) and a MBC50 of 11.8 μg/ml (10.0-13.6). These results may infer that furvina exhibits an in vitro bactericidal activity against sensitive and resistant bacteria from canine skin lesions

Predicción de la afinidad de ligandos antagonistas por receptores de adenosina A2A usando árboles de decisión

Neurodegenerative diseases are being treated by modulating adenosine receptors with more effective, safe and selective antagonists. The objective of the study was to develop a methodology to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds to qualitatively predict ligand-RAA2A affinity. For this purpose, a non-congeneric database of 315 antagonists was constructed and cured with its inhibition constant in nano molar, labeled as potent and weak. The Dragon and ISIDA / QSPR programs were used to calculate molecular descriptors and five groups of descriptors were obtained. In each group 50 descriptors were selected using the mRMR criterion. The database was divided into Training, Test and External series through a random selection and a generalized k-means cluster analysis. Classifiers were developed and validated using the WEKA program. The results were analyzed using the statistical tests of Friedman and Wilcoxon. The significant influence of parameter m of algorithm J48 on the predictivity was verified for the models that used the descriptors of the aug.a-b and hyb.aug.a groups of ISIDA / QSPR. The best performance model was obtained from the selected descriptors of the ISIDA-all group with a value of m = 6 and reached 90.6% prediction on the External series. The methodology developed to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds is effective in qualitatively predicting ligand-RAA2A affinity with accuracy, specificity and selectivity greater than 90 %. Keywords: classification, machine learning, modeling, QSARLas enfermedades neurodegenerativas están siendo tratadas mediante la modulación de los receptores de adenosina con antagonistas más eficaces, seguros y selectivos. El objetivo del estudio consistió en desarrollar una metodología para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos para predecir cualitativamente la afinidad ligando-RAA2A. Para ello se construyó y curó una base de datos no congenérica de 315 antagonistas con su constante de inhibición en nano molar, etiquetados como potentes y débiles. Se utilizaron los programas Dragon e ISIDA/QSPR para calcular descriptores moleculares y se obtuvieron cinco grupos de descriptores. En cada grupo se seleccionaron 50 descriptores usando el criterio mRMR. La base de datos se dividió en series de Entrenamiento, Prueba y Externa mediante una selección aleatoria y un análisis de clúster k-means generalizado. Se desarrollaron y validaron clasificadores utilizando el programa WEKA. Los resultados fueron analizados mediante las pruebas estadísticas de Friedman y Wilcoxon. Se comprobó la influencia significativa del parámetro m del algoritmo J48 en la predictividad, para los modelos que usaron los descriptores de los grupos aug.a-b e hyb.aug.a del ISIDA/QSPR. El modelo de mejor desempeño se obtuvo de los descriptores seleccionados del grupo ISIDA-todos con un valor de m=6 y alcanzó 90.6% de predicción sobre la serie Externa. La metodología desarrollada para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos es efectiva para predecir cualitativamente la afinidad ligando-RAA2A con una exactitud, especificidad y selectividad superiores al 90%. Palabras clave: aprendizaje automatizado; clasificación; modelación; QSA

Combining molecular docking and QSAR studies for modelling the antigyrase activity of cyclothialidine derivatives

DNA gyrase is a well-established antibacterial target consisting of two subunits, GyrA and GyrB, in a heterodimer A(2)B(2), where GyrB catalyzes the hydrolysis of ATP. Cyclothialidine (Ro 09-1437) has been considered as a promising inhibitor whose modifications might lead to more potent compounds against the enzyme. We report here for the first time, QSAR studies regarding to ATPase inhibitors of DNA Gyrase. 1D, 2D and 3D descriptors from DRAGON software were used on a set of 42 cyclothialidine derivatives. Based on the core of the cyclothialidine GR122222X, different conformations were created by using OMEGA. FRED was used to dock these conformers in the cavity of the GyrB subunit to select the best conformations, paying special attention to the 12-membered ring. Three QSAR models were developed considering the dimension of the descriptors. The models were robust, predictive and good in statistical significance, over 70% of the experimental variance was explained. Interpretability of the models was possible by extracting the SAR(s) encoded by these predictive models. Analyzing the compound-enzyme interactions of the complexes obtained by docking allowed us to increase the reliability of the information obtained for the QSAR models.status: publishe

Biodegradation of Crystalline Cellulose Nanofibers by Means of Enzyme Immobilized-Alginate Beads and Microparticles

Recent advances in nanocellulose technology have revealed the potential of crystalline cellulose nanofibers to reinforce materials which are useful for tissue engineering, among other functions. However, the low biodegradability of nanocellulose can possess some problems in biomedical applications. In this work, alginate particles with encapsulated enzyme cellulase extracted from Trichoderma reesei were prepared for the biodegradation of crystalline cellulose nanofibers, which carrier system could be incorporated in tissue engineering biomaterials to degrade the crystalline cellulose nanoreinforcement in situ and on-demand during tissue regeneration. Both alginate beads and microparticles were processed by extrusion-dropping and inkjet-based methods, respectively. Processing parameters like the alginate concentration, concentration of ionic crosslinker Ca2+, hardening time, and ionic strength of the medium were varied. The hydrolytic activity of the free and encapsulated enzyme was evaluated for unmodified (CNFs) and TEMPO-oxidized cellulose nanofibers (TOCNFs) in suspension (heterogeneous conditions); in comparison to solubilized cellulose derivatives (homogeneous conditions). The enzymatic activity was evaluated for temperatures between 25–75 °C, pH range from 3.5 to 8.0 and incubation times until 21 d. Encapsulated cellulase in general displayed higher activity compared to the free enzyme over wider temperature and pH ranges and for longer incubation times. A statistical design allowed optimizing the processing parameters for the preparation of enzyme-encapsulated alginate particles presenting the highest enzymatic activity and sphericity. The statistical analysis yielded the optimum particles characteristics and properties by using a formulation of 2% (w/v) alginate, a coagulation bath of 0.2 M CaCl2 and a hardening time of 1 h. In homogeneous conditions the highest catalytic activity was obtained at 55 °C and pH 4.8. These temperature and pH values were considered to study the biodegradation of the crystalline cellulose nanofibers in suspension. The encapsulated cellulase preserved its activity for several weeks over that of the free enzyme, which latter considerably decreased and practically showed deactivation after just 10 d. The alginate microparticles with their high surface area-to-volume ratio effectively allowed the controlled release of the encapsulated enzyme and thereby the sustained hydrolysis of the cellulose nanofibers. The relative activity of cellulase encapsulated in the microparticles leveled-off at around 60% after one day and practically remained at that value for three weeks

TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new anti-inflammatory compounds

A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general `in silico' technique to experimentation in anti-inflammatory discovery. The TOPological Sub-Structural Molecular Design (TOPS-MODE) approach has been applied to the study of the anti-inflammatory compounds. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441)

Classifier Ensemble Based on Feature Selection and Diversity Measures for Predicting the Affinity of A_2B Adenosine Receptor Antagonists

A_2B adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A_2B adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The <i>k</i>-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A_2B adenosine receptor antagonists, and it can be used to develop other QSAR models

Antileishmanial activity of 5-nitroindazole derivatives

Background: Currently, there is no safe and effective vaccine against leishmaniasis and existing therapies are inadequate due to high toxicity, cost and decreased efficacy caused by the emergence of resistant parasite strains. Some indazole derivatives have shown in vitro and in vivo activity against Trichomonas vaginalis and Trypanosoma cruzi . On that basis, 20 indazole derivatives were tested in vitro against Leishmania amazonensis . Objective: To evaluate the in vitro activity of twenty 2-benzyl-5-nitroindazolin-3-one derivatives against L. amazonensis . Design: For the selection of promising compounds, it is necessary to evaluate the indicators for in vitro activity. For this aim, a battery of studies for antileishmanial activity and cytotoxicity were implemented. These results enabled the determination of the substituents in the indazole derivatives responsible for activity and selectivity, through the analysis of the structure–activity relationship (SAR). Methods: In vitro cytotoxicity against mouse peritoneal macrophages and growth inhibitory activity in promastigotes were evaluated for 20 compounds. Compounds that showed adequate selectivity were tested against intracellular amastigotes. The SAR from the results in promastigotes was represented using the SARANEA software. Results: Eight compounds showed selectivity index >10% and 50% inhibitory concentration <1 µM against the promastigote stage. Against intracellular amastigotes, four were as active as Amphotericin B. The best results were obtained for 2-(benzyl-2,3-dihydro-5-nitro-3-oxoindazol-1-yl) ethyl acetate, with 50% inhibitory concentration of 0.46 ± 0.01 µM against amastigotes and a selectivity index of 875. The SAR study showed the positive effect on the selectivity of the hydrophilic fragments substituted in position 1 of 2-benzyl-5- nitroindazolin-3-one, which played a key role in improving the selectivity profile of this series of compounds. Conclusion: 2-bencyl-5-nitroindazolin-3-one derivatives showed selective and potent in vitro activity, supporting further investigations on this family of compounds as potential antileishmanial hits