Modeling anti-Trypanosoma cruzi Activity of N-Oxide Containing Heterocycles

Ligand-based design, synthesis, and experimental evaluation of novel benzofuroxan derivatives as anti-Trypanosoma cruzi agents

A set of substituted-[N'-(benzofuroxan-5-yl)methylene]benzohydrazides (4a-t), previously designed and synthesized, was experimentally assayed against Trypanosoma cruzi, the etiological agent of Chagas' disease, one of the most neglected tropical diseases. Exploratory data analysis, Hansch approach and VolSurf formalism were applied to aid the ligand-based design of novel anti-T. cruzi agents. The best 2D-QSAR model showed suitable statistical measures [n = 18; s = 0.11; F = 42.19; R(2) = 0.90 and Q(2) = 0.77 (SDEP = 0.15)], and according to the optimum 3D-QSAR model [R(2) = 0.98, Q(2) = 0.93 (SDEP = 0.08)], three latent variables explained 62% of the total variance from original data. Steric and hydrophobic properties were pointed out as the key for biological activity. Based upon the findings, six novel benzofuroxan derivatives (4u-z) were designed, synthesized, and in vitro assayed to perform the QSAR external prediction. Then, the predictability for the both models, 2D-QSAR (Rpred(2) = 0.91) and 3D-QSAR (Rpred(2) = 0.77), was experimentally validated, and compound 4u was identified as the most active anti-T. cruzi hit (IC50 = 3.04 μM).

Classification models for safe drug molecules

Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.

3-Trifluoromethylquinoxaline N,N'-dioxides as anti-trypanosomatid agents. Identification of optimal anti-T. cruzi agents and mechanism of action studies

For a fourth approach of quinoxaline N,N'-dioxides as anti-trypanosomatid agents against T. cruzi and Leishmania, we found extremely active derivatives. The present study allows us to state the correct requirements for obtaining optimal in vitro anti-T. cruzi activity. Derivatives possessing electron-withdrawing substituents in the 2-, 3-, 6-, and 7-positions were the most active compounds. With regard to these features and taking into account their mammal cytotoxicity, some trifluoromethylquinoxaline N,N'-dioxides have been proposed as candidates for further clinical studies. Consequently, mutagenicity and in vivo analyses were performed with the most promising derivatives. In addition, with regard to the mechanism of action studies, it was demonstrated that mitochondrial dehydrogenases are involved in the anti-T. cruzi activity of the most active derivatives.

Anti-T. cruzi activities and QSAR studies of 3-arylquinoxaline-2-carbonitrile di-N-oxides

In a continuing effort to identify new active compounds for combating Chagas disease and other neglected diseases, our research group synthesized and evaluated 23 3-arylquinoxaline-2-carbonitrile di-N-oxides against Trypanosoma cruzi. Five of them presented IC(50) values of the same magnitude as the standard drug Nifurtimox, making them valid as new lead compounds. The optimized molecular structures of 23 derivatives represented by 1497 types of DRAGON descriptors were subjected to linear regression analysis, and the derived QSAR was shown to be predictive. In this way, we achieved a rational guide for the proposal of new candidate structures whose activities still remain unknown.

In silico prediction and screening of gamma-secretase inhibitors by molecular descriptors and machine learning methods

Gamma-secretase inhibitors have been explored for the prevention and treatment of Alzheimer's disease (AD). Methods for prediction and screening of gamma-secretase inhibitors are highly desired for facilitating the design of novel therapeutic agents against AD, especially when incomplete knowledge about the mechanism and three-dimensional structure of gamma-secretase. We explored two machine learning methods, support vector machine (SVM) and random forest (RF), to develop models for predicting gamma-secretase inhibitors of diverse structures. Quantitative analysis of the receiver operating characteristic (ROC) curve was performed to further examine and optimize the models. Especially, the Youden index (YI) was initially introduced into the ROC curve of RF so as to obtain an optimal threshold of probability for prediction. The developed models were validated by an external testing set with the prediction accuracies of SVM and RF 96.48 and 98.83% for gamma-secretase inhibitors and 98.18 and 99.27% for noninhibitors, respectively. The different feature selection methods were used to extract the physicochemical features most relevant to gamma-secretase inhibition. To the best of our knowledge, the RF model developed in this work is the first model with a broad applicability domain, based on which the virtual screening of gamma-secretase inhibitors against the ZINC database was performed, resulting in 368 potential hit candidates.

Hybrid-genetic algorithm based descriptor optimization and QSAR models for predicting the biological activity of Tipranavir analogs for HIV protease inhibition

The prediction of biological activity of a chemical compound from its structural features plays an important role in drug design. In this paper, we discuss the quantitative structure activity relationship (QSAR) prediction models developed on a dataset of 170 HIV protease enzyme inhibitors. Various chemical descriptors that encode hydrophobic, topological, geometrical and electronic properties are calculated to represent the structures of the molecules in the dataset. We use the hybrid-GA (genetic algorithm) optimization technique for descriptor space reduction. The linear multiple regression analysis (MLR), correlation-based feature selection (CFS), non-linear decision tree (DT), and artificial neural network (ANN) approaches are used as fitness functions. The selected descriptors represent the overall descriptor space and account well for the binding nature of the considered dataset. These selected features are also human interpretable and can be used to explain the interactions between a drug molecule and its receptor protein (HIV protease). The selected descriptors are then used for developing the QSAR prediction models by using the MLR, DT and ANN approaches. These models are discussed, analyzed and compared to validate and test their performance for this dataset. All three approaches yield the QSAR models with good prediction performance. The models developed by DT and ANN are comparable and have better prediction than the MLR model. For ANN model, weight analysis is carried out to analyze the role of various descriptors in activity prediction. All the prediction models point towards the involvement of hydrophobic interactions. These models can be useful for predicting the biological activity of new untested HIV protease inhibitors and virtual screening for identifying new lead compounds.

Mode of action of nifurtimox and N-oxide-containing heterocycles against Trypanosoma cruzi: is oxidative stress involved?

Chagas disease is caused by the trypanosomatid parasite Trypanosoma cruzi and threatens millions of lives in South America. As other neglected diseases there is almost no research and development effort by the pharmaceutical industry and the treatment relies on two drugs, Nifurtimox and Benznidazole, discovered empirically more than three decades ago. Nifurtimox, a nitrofurane derivative, is believed to exert its biological activity through the bioreduction of the nitro-group to a nitro-anion radical which undergoes redox-cycling with molecular oxygen. This hypothesis is generally accepted, although arguments against it have been presented. In the present work we studied the ability of Nifurtimox and five N-oxide-containing heterocycles to induce oxidative stress in T. cruzi. N-Oxide-containing heterocycles represent a promising group of new trypanosomicidal agents and their mode of action is not completely elucidated. The results here obtained argue against the oxidative stress hypothesis almost for all the studied compounds, including Nifurtimox. A significant reduction in the level of parasitic low-molecular-weight thiols was observed after Nifurtimox treatment; however, it was not linked to the production of reactive oxidant species. Besides, redox-cycling is only observed at high Nifurtimox concentrations (>400microM), two orders of magnitude higher than the concentration required for anti-proliferative activity (5microM). Our results indicate that an increase in oxidative stress is not the main mechanism of action of Nifurtimox. Among the studied N-oxide-containing heterocycles, benzofuroxan derivatives strongly inhibited parasite dehydrogenase activity and affected mitochondrial membrane potential. The indazole derivative raised intracellular oxidants production, but it was the least effective as anti-T. cruzi.