Efecto inmunomodulador de saponinas extraídas de Agave brittoniana subsp. brachypus (Trel.) en modelo de toxicidad alcohólica fetal / Immunomodulatory effect of saponins extracted from Agave brittoniana subsp. brachypus (Trel.) in fetal alcohol toxicity model

Diversos productos naturales y sus metabolitos constituyen fuente importante de moléculas con propiedades inmunomoduladoras. La especie Agave brittoniana subsp. brachypus (Trel.) A. Álvarez presenta dentro de los metabolitos secundarios las saponinas esteroidales, con potencial actividad inmunomoduladora. Se realizó una investigación de tipo experimental para evaluar el efecto inmunomodulador de un extracto hidroalcohólico de A. brittoniana (100 mg/Kg) en animales sanos y de saponinas de la especie (25, 50 y 100 mg/kg) en ratas neonatos expuestas a toxicidad alcohólica prenatal, en ambos modelos se utilizó como control al ácido fólico. El extracto hidroalcohólico del A. brittoniana T. aumentó la respuesta de la inmunidad humoral en animales sanos sin alterar la respuesta celular. Se observó en neonatos inmunocomprometidos por toxicidad alcohólica fetal, un incremento de leucocitos a expensas de los linfocitos en el grupo de mayor dosis de las saponinas. La respuesta humoral de estos neonatos se caracterizó por incremento de IgG en las tres dosis y un restablecimiento de los valores de C3 solo en el grupo de 100 mg/kg de saponinas de A. brittoniana. La morfología del timo en animales sanos tratados con A. brittoniana no fue modificada, a diferencia del timo de neonatos sometidos a toxicidad alcohólica prenatal donde se apreció incremento de la zona cortical. Se concluye que la planta tiene elevado potencial inmunomodulador tanto en animales sanos como en inmunocomprometidos.

Computational Identification of Chemical Compounds with Potential Activity against Leishmania amazonensis using Nonlinear Machine Learning Techniques.

Leishmaniasis is a poverty-related disease endemic in 98 countries worldwide, with morbidity and mortality increasing daily. All currently used first-line and second-line drugs for the treatment of leishmaniasis exhibit several drawbacks including toxicity, high costs and route of administration. Consequently, the development of new treatments for leishmaniasis is a priority in the field of neglected tropical diseases. The aim of this work is to develop computational models those allow the identification of new chemical compounds with potential anti-leishmanial activity. A data set of 116 organic chemicals, assayed against promastigotes of Leishmania amazonensis, is used to develop the theoretical models. The cutoff value to consider a compound as active one was IC50≤1.5µM. For this study, we employed Dragon software to calculate the molecular descriptors and WEKA to obtain machine learning (ML) models. All ML models showed accuracy values between 82% and 91%, for the training set. The models developed with k-nearest neighbors and classification trees showed sensitivity values of 97% and 100%, respectively; while the models developed with artificial neural networks and support vector machine showed specificity values of 94% and 92%, respectively. In order to validate our models, an external test-set was evaluated with good behavior for all models. A virtual screening was performed and 156 compounds were identified as potential anti-leishmanial by all the ML models. This investigation highlights the merits of ML-based techniques as an alternative to other more traditional methods to find new chemical compounds with anti-leishmanial activity.

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

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 µM, while for the benznidazole the IC50 = 54.7 µM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.

A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis.

The neglected tropical diseases (NTDs) affect more than one billion people (one-sixth of the world's population) and occur primarily in undeveloped countries in sub-Saharan Africa, Asia, and Latin America. Available drugs for these diseases are decades old and present an important number of limitations, especially high toxicity and, more recently, the emergence of drug resistance. In the last decade several Quantitative Structure-Activity Relationship (QSAR) studies have been developed in order to identify new organic compounds with activity against the parasites responsible for these diseases, which are reviewed in this paper. The topics summarized in this work are: 1) QSAR studies to identify new organic compounds actives against Chaga's disease; 2) Development of QSAR studies to discover new antileishmanial drusg; 3) Computational studies to identify new drug-like compounds against human African trypanosomiasis. Each topic include the general characteristics, epidemiology and chemotherapy of the disease as well as the main QSAR approaches to discovery/identification of new actives compounds for the corresponding neglected disease. The last section is devoted to a new approach know as multi-target QSAR models developed for antiparasitic drugs specifically those actives against trypanosomatid parasites. At present, as a result of these QSAR studies several promising compounds, active against these parasites, are been indentify. However, more efforts will be required in the future to develop more selective (specific) useful drugs.

Ligand-based discovery of novel trypanosomicidal drug-like compounds: in silico identification and experimental support.

Two-dimensional bond-based linear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A database with 143 anti-trypanosomal and 297 compounds having other clinical uses, are utilized to develop the theoretical models. The best discriminant models computed using bond-based linear indices provides accuracies greater than 90 for both training and test sets. Our models identify as anti-trypanosomals five out of nine compounds of a set of already-synthesized substances. The in vitro anti-trypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a perfect agreement between theoretical predictions and experimental results. The compounds identified as active ones show more than 98% of anti-epimastigote elimination (AE) at a concentration of 100 µg/mL. Besides, three compounds show more than 70% of AE at a concentration of 10 µg/mL. Finally, compounds with the best "activity against epimastigote forms/unspecific cytotoxicity" ratio are evaluated using an amastigote susceptibility assay. It should be noticed that, compound Va7-71 exhibit a 100% of intracellular amastigote elimination and shows similar activity when compared to a standard trypanosomicidal as nifurtimox. Finally, we can emphasize that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new anti-trypanosomal compounds.

Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis.

Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 microg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 microg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.