Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Ligand and structure-based discovery of phosphorus-containing compounds as potential metalloproteinase inhibitors

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Anti-inflammatory Activity: In silico and In vivo of Sapogenins Present in Agave brittoniana subsp. brachypus (Trel.)

BACKGROUND

Agave brittoniana subsp. brachypus is an endemic plant of Cuba, which contains different steroidal sapogenins with anti-inflammatory effects. This work aims to develop computational models which allow the identification of new chemical compounds with potential anti-inflammatory activity.

METHODS

The in vivo anti-inflammatory activity was evaluated in two rat models: carrageenaninduced paw edema and cotton pellet-induced granuloma. In each study, we used 30 Sprague Dawley male rats divided into five groups containing six animals. The products isolated and administrated were fraction rich in yuccagenin and sapogenins crude.

RESULTS

The obtained model, based on a classification tree, showed an accuracy value of 86.97% for the training set. Seven compounds (saponins and sapogenins) were identified as potential antiinflammatory agents in the virtual screening. According to in vivo studies, the yuccagenin-rich fraction was the greater inhibitor of the evaluated product from Agave.

CONCLUSION

The evaluated metabolites of the Agave brittoniana subsp. Brachypus showed an interesting anti-inflammatory effect.

Machine learning approach to discovery of small molecules with potential inhibitory action against vasoactive metalloproteases

With the advancement of combinatorial chemistry and big data, drug repositioning has boomed. In this sense, machine learning and artificial intelligence techniques offer a priori information to identify the most promising candidates. In this study, we combine QSAR and docking methodologies to identify compounds with potential inhibitory activity of vasoactive metalloproteases for the treatment of cardiovascular diseases. To develop this study, we used a database of 191 thermolysin inhibitor compounds, which is the largest as far as we know. First, we use Dragon's molecular descriptors (0-3D) to develop classification models using Bayesian networks (Naive Bayes) and artificial neural networks (Multilayer Perceptron). The obtained models are used for virtual screening of small molecules in the international DrugBank database. Second, docking experiments are carried out for all three enzymes using the Autodock Vina program, to identify possible interactions with the active site of human metalloproteases. As a result, high-performance artificial intelligence QSAR models are obtained for training and prediction sets. These allowed the identification of 18 compounds with potential inhibitory activity and an adequate oral bioavailability profile, which were evaluated using docking. Four of them showed high binding energies for the three enzymes, and we propose them as potential dual ACE/NEP inhibitors for the control of blood pressure. In summary, the in silico strategies used here constitute an important tool for the early identification of new antihypertensive drug candidates, with substantial savings in time and money.

Computational identification of chemical compounds with potential anti-Chagas activity using a classification tree

Chagas disease is endemic to 21 Latin American countries and is a great public health problem in that region. Current chemotherapy remains unsatisfactory; consequently the need to search for new drugs persists. Here we present a new approach to identify novel compounds with potential anti-chagasic action. A large dataset of 584 compounds, obtained from the Drugs for Neglected Diseases initiative, was selected to develop the computational model. Dragon software was used to calculate the molecular descriptors and WEKA software to obtain the classification tree. The best model shows accuracy greater than 93.4% for the training set; the tree was also validated using a 10-fold cross-validation procedure and through a test set, achieving accuracy values over 90.5% and 92.2%, correspondingly. The values of sensitivity and specificity were around 90% in all series; also the false alarm rate values were under 10.5% for all sets. In addition, a simulated ligand-based virtual screening for several compounds recently reported as promising anti-chagasic agents was carried out, yielding good agreement between predictions and experimental results. Finally, the present work constitutes an example of how this rational computer-based method can help reduce the cost and increase the rate in which novel compounds are developed against Chagas disease.

Thorough evaluation of OECD principles in modelling of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine derivatives using QSARINS

The human immunodeficiency virus is a lethal pathology considered as a worldwide problem. The search for new strategies for the treatment of this disease continues to be a great challenge in the scientific community. In this study, a series of 107 derivatives of 1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine, previously evaluated experimentally against HIV-I reverse transcriptase, was used to model antiretroviral activity. A model of linear regression, implemented in the QSARINS software, was developed with a genetic algorithm for variable selection. The fit of its parameters was good and exhaustive validation, according to the OECD regulatory principles, was performed. Also, the applicability domain was established. In addition, its robustness (r ² = 0.84), stability (Q ² _LOO = 0.81; Q ² _LMO = 0.80) and good predictive power (r ² _EXT = 0.85) is proved. So, it was used to predict the antiretroviral activity of eight compounds obtained by rational drug design. Finally, it can be affirmed that the proposed tools allow the rapid and economic identification of potential antiretroviral drugs.

Experimental models in Chagas disease: a review of the methodologies applied for screening compounds against Trypanosoma cruzi

One of the main problems of Chagas disease (CD), the parasitic infection caused by Trypanosoma cruzi, is the lack of a completely satisfactory treatment, which is currently based on two old nitroheterocyclic drugs (i.e., nifurtimox and benznidazole) that show important limitations for treating patients. In this context, many laboratories look for alternative therapies potentially applicable to the treatment, and therefore, research in CD chemotherapy works in the design of experimental protocols for detecting molecules with activity against T. cruzi. Phenotypic assays are considered the most valuable strategy for screening these antiparasitic compounds. Among them, in vitro experiments are the first step to test potential anti-T. cruzi drugs directly on the different parasite forms (i.e., epimastigotes, trypomastigotes, and amastigotes) and to detect cytotoxicity. Once the putative trypanocidal drug has been identified in vitro, it must be moved to in vivo models of T. cruzi infection, to explore (i) acute toxicity, (ii) efficacy during the acute infection, and (iii) efficacy in the chronic disease. Moreover, in silico approaches for predicting activity have emerged as a supporting tool for drug screening procedures. Accordingly, this work reviews those in vitro, in vivo, and in silico methods that have been routinely applied during the last decades, aiming to discover trypanocidal compounds that contribute to developing more effective CD treatments.

Prediction of aquatic toxicity of benzene derivatives using molecular descriptor from atomic weighted vectors

Several descriptors from atom weighted vectors are used in the prediction of aquatic toxicity of set of organic compounds of 392 benzene derivatives to the protozoo ciliate Tetrahymena pyriformis (log(IGC50)^-1). These descriptors are calculated using the MD-LOVIs software and various Aggregation Operators are examined with the aim comparing their performances in predicting aquatic toxicity. Variability analysis is used to quantify the information content of these molecular descriptors by means of an information theory-based algorithm. Multiple Linear Regression with Genetic Algorithms is used to obtain models of the structure-toxicity relationships; the best model shows values of Q²=0.830 and R²=0.837 using six variables. Our models compare favorably with other previously published models that use the same data set. The obtained results suggest that these descriptors provide an effective alternative for determining aquatic toxicity of benzene derivatives.

Machine learning-based models to predict modes of toxic action of phenols to Tetrahymena pyriformis

The phenols are structurally heterogeneous pollutants and they present a variety of modes of toxic action (MOA), including polar narcotics, weak acid respiratory uncouplers, pro-electrophiles, and soft electrophiles. Because it is often difficult to determine correctly the mechanism of action of a compound, quantitative structure-activity relationship (QSAR) methods, which have proved their interest in toxicity prediction, can be used. In this work, several QSAR models for the prediction of MOA of 221 phenols to the ciliated protozoan Tetrahymena pyriformis, using Chemistry Development Kit descriptors, are reported. Four machine learning techniques (ML), k-nearest neighbours, support vector machine, classification trees, and artificial neural networks, have been used to develop several models with higher accuracies and predictive capabilities for distinguishing between four MOAs. They showed global accuracy values between 95.9% and 97.7% and area under Receiver Operator Curve values between 0.978 and 0.998; additionally, false alarm rate values were below 8.2% for training set. In order to validate our models, cross-validation (10-folds-out) and external test-set were performed with good behaviour in all cases. These models, obtained with ML techniques, were compared with others previously reported by other researchers, and the improvement was significant.

Computer-Aided Drug Discovery Approaches against the Tropical Infectious Diseases Malaria, Tuberculosis, Trypanosomiasis, and Leishmaniasis

Despite the tremendous improvement in overall global health heralded by the adoption of the Millennium Declaration in the year 2000, tropical infections remain a major health problem in the developing world. Recent estimates indicate that the major tropical infectious diseases, namely, malaria, tuberculosis, trypanosomiasis, and leishmaniasis, account for more than 2.2 million deaths and a loss of approximately 85 million disability-adjusted life years annually. The crucial role of chemotherapy in curtailing the deleterious health and economic impacts of these infections has invigorated the search for new drugs against tropical infectious diseases. The research efforts have involved increased application of computational technologies in mainstream drug discovery programs at the hit identification, hit-to-lead, and lead optimization stages. This review highlights various computer-aided drug discovery approaches that have been utilized in efforts to identify novel antimalarial, antitubercular, antitrypanosomal, and antileishmanial agents. The focus is largely on developments over the past 5 years (2010-2014).