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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.

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Although several scores stratify venous thromboembolism (VTE) risk in solid tumors, hematologic malignancies (HM) are underrepresented. To develop an internal and external validation of a logistic regression model to predict VTE risk in hospitalized HM patients. Validation of the existing VTE predictive model was performed through a prospective case-control study in 496 hospitalized HM patients between December 2010 and 2020 at the Arnaldo Milián University Hospital, Cuba. The predictive model designed with data from 285 patients includes 5 predictive factors: hypercholesterolemia, tumoral activity, use of thrombogenic drugs, diabetes mellitus, and immobilization. The model was internally validated using bootstrap analysis. External validation was realized in a prospective cohort of 211 HM patients. The predictive model had a 76.4% negative predictive value (NPV) and an 81.7% positive predictive value (PPV) in the bootstrapping validation. The area under curve (AUC) in the bootstrapping set was 0.838. Accuracy was 80.1% and 82.9% in the internal and external validation, respectively. In the external validation, the model produced 89.7% of NPV, 67.7% of PPV, 74.6% of sensitivity, and 86.2% of specificity. The AUC in the external validation was 0.900. VTE predictive model is a reproducible and simple tool with good accuracy and discrimination.

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In silico prediction of antileishmanial activity using quantitative structure-activity relationship (QSAR) models has been developed on limited and small datasets. Nowadays, the availability of large and diverse high-throughput screening data provides an opportunity to the scientific community to model this activity from the chemical structure. In this study, we present the first KNIME automated workflow to modeling a large, diverse, and highly imbalanced dataset of compounds with antileishmanial activity. Because the data is strongly biased toward inactive compounds, a novel strategy was implemented based on the selection of different balanced training sets and a further consensus model using single decision trees as the base model and three criteria for output combinations. The decision tree consensus was adopted after comparing its classification performance to consensuses built upon Gaussian-Naïve-Bayes, Support-Vector-Machine, Random-Forest, Gradient-Boost, and Multi-Layer-Perceptron base models. All these consensuses were rigorously validated using internal and external test validation sets and were compared against each other using Friedman and Bonferroni-Dunn statistics. For the retained decision tree-based consensus model, which covers 100% of the chemical space of the dataset and with the lowest consensus level, the overall accuracy statistics for test and external sets were between 71 and 74% and 71 and 76%, respectively, while for a reduced chemical space (21%) and with an incremental consensus level, the accuracy statistics were substantially improved with values for the test and external sets between 86 and 92% and 88 and 92%, respectively. These results highlight the relevance of the consensus model to prioritize a relatively small set of active compounds with high prediction sensitivity using the Incremental Consensus at high level values or to predict as many compounds as possible, lowering the level of Incremental Consensus. Finally, the workflow developed eliminates human bias, improves the procedure reproducibility, and allows other researchers to reproduce our design and use it in their own QSAR problems.

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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 k-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.

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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.

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Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

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The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.

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