New NADPH Oxidase 2 Inhibitors Display Potent Activity against Oxidative Stress by Targeting p22phox-p47phox Interactions.

NADPH oxidase (NOX2) is responsible for reactive oxygen species (ROS) production in neutrophils and has been recognized as a key mediator in inflammatory and cardiovascular pathologies. Nevertheless, there is a lack of specific NOX2 pharmacological inhibitors. In medicinal chemistry, heterocyclic compounds are essential scaffolds for drug design, and among them, indole is a very versatile pharmacophore. We tested the hypothesis that indole heteroaryl-acrylonitrile derivatives may serve as NOX2 inhibitors by evaluating the capacity of 19 of these molecules to inhibit NOX2-derived ROS production in human neutrophils (HL-60 cells). Of these compounds, C6 and C14 exhibited concentration-dependent inhibition of NOX2 (IC50~1 µM). These molecules also reduced NOX2-derived oxidative stress in cardiomyocytes and prevented cardiac damage induced by ischemia-reperfusion. Compound C6 significantly reduced the membrane translocation of p47phox, a cytosolic subunit that is required for NOX2 activation. Molecular docking analyses of the binding modes of these molecules with p47phox indicated that C6 and C14 interact with specific residues in the inner part of the groove of p47phox, the binding cavity for p22phox. This combination of methods showed that novel indole heteroaryl acrylonitriles represent interesting lead compounds for developing specific and potent NOX2 inhibitors.

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.

Chemoinformatics Profiling of the Chromone Nucleus as a MAO-B/A2AAR Dual Binding Scaffold.

BACKGROUND: In the context of the current drug discovery efforts to find disease modifying therapies for Parkinson's disease (PD) the current single target strategy has proved inefficient. Consequently, the search for multi-potent agents is attracting more and more attention due to the multiple pathogenetic factors implicated in PD. Multiple evidences points to the dual inhibition of the monoamine oxidase B (MAO-B), as well as adenosine A2A receptor (A2AAR) blockade, as a promising approach to prevent the neurodegeneration involved in PD. Currently, only two chemical scaffolds has been proposed as potential dual MAO-B inhibitors/A2AAR antagonists (caffeine derivatives and benzothiazinones). METHODS: In this study, we conduct a series of chemoinformatics analysis in order to evaluate and advance the potential of the chromone nucleus as a MAO-B/A2AAR dual binding scaffold. RESULTS: The information provided by SAR data mining analysis based on network similarity graphs and molecular docking studies support the suitability of the chromone nucleus as a potential MAOB/ A2AAR dual binding scaffold. Additionally, a virtual screening tool based on a group fusion similarity search approach was developed for the prioritization of potential MAO-B/A2AAR dual binder candidates. Among several data fusion schemes evaluated, the MEAN-SIM and MIN-RANK GFSS approaches demonstrated to be efficient virtual screening tools. Then, a combinatorial library potentially enriched with MAO-B/A2AAR dual binding chromone derivatives was assembled and sorted by using the MIN-RANK and then the MEAN-SIM GFSS VS approaches. CONCLUSION: The information and tools provided in this work represent valuable decision making elements in the search of novel chromone derivatives with a favorable dual binding profile as MAOB inhibitors and A2AAR antagonists with the potential to act as a disease-modifying therapeutic for Parkinson's disease.

Probing the Hypothesis of SAR Continuity Restoration by the Removal of Activity Cliffs Generators in QSAR.

In this work we report the first attempt to study the effect of activity cliffs over the generalization ability of machine learning (ML) based QSAR classifiers, using as study case a previously reported diverse and noisy dataset focused on drug induced liver injury (DILI) and more than 40 ML classification algorithms. Here, the hypothesis of structure-activity relationship (SAR) continuity restoration by activity cliffs removal is tested as a potential solution to overcome such limitation. Previously, a parallelism was established between activity cliffs generators (ACGs) and instances that should be misclassified (ISMs), a related concept from the field of machine learning. Based on this concept we comparatively studied the classification performance of multiple machine learning classifiers as well as the consensus classifier derived from predictive classifiers obtained from training sets including or excluding ACGs. The influence of the removal of ACGs from the training set over the virtual screening performance was also studied for the respective consensus classifiers algorithms. In general terms, the removal of the ACGs from the training process slightly decreased the overall accuracy of the ML classifiers and multi-classifiers, improving their sensitivity (the weakest feature of ML classifiers trained with ACGs) but decreasing their specificity. Although these results do not support a positive effect of the removal of ACGs over the classification performance of ML classifiers, the "balancing effect" of ACG removal demonstrated to positively influence the virtual screening performance of multi-classifiers based on valid base ML classifiers. Specially, the early recognition ability was significantly favored after ACGs removal. The results presented and discussed in this work represent the first step towards the application of a remedial solution to the activity cliffs problem in QSAR studies.