Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective.

The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds.

Revisión de los modelos computacionales que relacionan la estructura química con la disrupción del sistema endocrino / Protection against endocrine disruption through quantitative structure-activity relationship modelling

El término interruptor endocrino define una amplia y diversa clase de sustancias de origen natural o antropogénico con la capacidad de interferir con alguna función del sistema endocrino y provocar efectos adversos en un organismo o su descendencia. La disrupción endocrina se asocia con cáncer, obesidad, diabetes, disfunción reproductiva e inmunológica. Constituye una forma específica de toxicidad cuyas regulaciones y legislación actualmente carecen de consenso. Los métodos computacionales, y particularmente los estudios quimioinformáticos como las relaciones cuantitativas estructura-actividad (QSAR), son herramientas valiosas de investigación que han ocupado gradualmente un importante espacio en los estudios toxicológicos. Esta revisión propone un análisis del más reciente estado del arte relativo a la modelación QSAR de la disrupción endocrina. Los casos de estudio seleccionados se centran en tres mecanismos importantes que representan la biosíntesis, el transporte y la interacción con los receptores hormonales mediados por la capacidad inhibitoria de la enzima aromatasa, y los efectos sobre la proteína transportadora de transtiretina y el receptor de andrógenos, respectivamente. Estas herramientas predictivas pueden ayudar a priorizar sustancias como posibles alteradores endocrinos y, por lo tanto, son contribuciones importantes que garantizan el ahorro de tiempo, materiales y recursos humanos

The endocrine disruptors are defined as a broad and diverse class of substances of natural or anthropogenic origin with the ability to interfere with some function of the endocrine system and, in doing so, cause adverse effects on an organism or its descendants. Endocrine disruption, associated with pathologies such as cancer, obesity, diabetes, and reproductive and immunological dysfunction, constitutes a specific form of toxicity whose regulation and legislation currently lack consensus. Computational methods, and within them chemoinformatic studies such as the prediction of quantitative structure-activity relationships (QSAR), are valuable research tools that have gradually occupied an important space in toxicological studies. This review proposes an analysis of the most recent state of the art related to QSAR modelling in the context of endocrine disruption. For this, case studies reported on three important hormonal mechanisms were selected, which represent synthesis, transport, and interaction with receptors. The summarized QSARs modelled the inhibitory capacity of the aromatase enzyme and the effects on the transthyretin transporter protein and the androgen receptor. These predictive tools can assist in prioritizing substances as potential endocrine disruptors and are therefore important contributions that guarantee the saving of time, material, and human resources

Métodos computacionales en toxicología predictiva: aplicación a la reducción de ensayos con animales en el contexto de la legislación comunitaria REACH / Computational methods in predictive toxicology: application to the reduction of animal tests in the context of the EU REACH regulation

Los métodos de química informática y modelado molecular han sido utilizados desde hace décadas para la selección y optimización de nuevos compuestos con propiedades terapéuticas. Su aplicación en toxicología predictiva es más reciente, y dadas las nuevas necesidades regulatorias impuestas por la normativa europea REACH, estas técnicas gozan actualmente de un interés creciente. En efecto, el reglamento REACH supone a priori la necesidad de una cantidad ingente de ensayos con animales para demostrar la seguridad de los nuevos compuestos químicos sometidos a registro, ensayos que pueden reducirse mediante el uso de métodos alternativos como los estudios in vitro e in silico, siempre que cumplan ciertas condiciones específicas que garanticen su calidad y eficacia predictiva. La toxicología computacional es pues una subdisciplina de la toxicología que tiene como objetivo utilizar las matemáticas, la estadística, el modelado químico y las herramientas informáticas para predecir los efectos tóxicos de las sustancias químicas en la salud humana y/o el medio ambiente, y adicionalmente comprender mejor los mecanismos por los que un producto químico determinado induce daño. En esta revisión resumimos el estado del arte de los diferentes métodos existentes en materia de toxicología computacional, citamos las bases de datos y programas más adecuados para la generación de predicciones robustas y fiables, y se discuten sus limitaciones y el grado de aceptación en el ámbito normativo (AU)

Molecular modeling and chemoinformatics have been used for decades for the selection and optimization of new compounds with therapeutic properties. The application of these techniques in predictive toxicology is more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by the EU REACH regulation. Indeed, a large amount of animal testing is needed under REACH to demonstrate the safety of new chemical entities subjected to registration, and these assays can be significantly reduced by using alternative in vitro and in silico methods, provided they meet specific conditions to ensure their quality and predictive power. Computational toxicology is as a subdiscipline of toxicology that aims to use mathematics, statistics, chemistry and computer modeling tools to predict the toxic effects of chemicals on human health and/or environment. Additionally, computation studies can help also to better understand the mechanisms by which a given chemical induces harm. In this review we summarize the state of art of the main in silico methods, the toxicological databases and computer programs more suitable for the generation of robust and reliable predictions will be listed, and the limitations and acceptability of computational toxicology will be discussed in the context of the UE regulation (AU)

[New methodologic approaches in antimalarial molecular research]. / Nouvelles approches méthodologiques dans la recherche de molécules antimalariques.

The estimated worldwide incidence of Plasmodium falciparum malaria is about 500 million cases a year. In tropical areas, the dramatic increase of resistance to most antimalarial drugs is directly responsible for persistent widespread high endemicity and related morbidity. The search to identify new drug targets and agents is a high priority. However the value of standard pharmacological research methods is greatly diminished by technical problems involving in vitro and in vivo modeling of malaria infection. In recent decades new mathematical tools have been developed to predict drug properties and to estimate biological activity in silico. Various approaches have been proposed based on 2D or 3D descriptions of the chemical structure of the drug and target followed by mathematical and statistical characterization of their interaction. These techniques are now widely used in medicinal chemistry and have proven their efficacy for screening the anti-malarial activity of numerous molecules in large databases and for virtual synthesis. Incorporating new knowledge from the genomic studies of Plasmodium has markedly increased the performance and range of application of these tools for identifying new drug targets against malaria.

Synthesis of new fluoroquinolones and evaluation of their in vitro activity on Toxoplasma gondii and Plasmodium spp.

The synthesis of four new computer-designed fluoroquinolones which have been predicted by QSAR analysis to be active against the protozoa Toxoplasma gondii is described. These compounds are inhibitory in vitro for T. gondii. One of these compounds has a remarkably high activity comparable to that of trovafloxacin. It combines the basic cyclopropyl-quinoline structure of gatifloxacin or moxifloxacin with the C-7 6-amino-3-azabicyclo[3.1.0]hexyl side chain of trovafloxacin. The four compounds are also inhibitory for blood stages of Plasmodium falciparum though at high concentration. These results confirm the potential of quinolones as anti-T. gondii and antimalarial drugs but also show that the QSAR models for T. gondii cannot be reliably extended for screening antimalarial activity.

Application of topological descriptors in QSAR and drug design: history and new trends.

Powerful methodologies for drug design and drug database screening and selection are presently available. Studies relating the structure of molecules to a property or a biological activity by means of statistical tools (QSPR and QSAR studies, respectively) are particularly relevant. An important point for this methodology is the use of good structural descriptors that are representative of the molecular features responsible for the relevant activity. Topological indices (TIs) are two-dimensional descriptors which take into account the internal atomic arrangement of compounds, and which encode in numerical form information about molecular size, shape, branching, presence of heteroatoms and multiple bonds. The usefulness of TIs in QSPR and QSAR studies has been extensively demonstrated, and they have also been used as a measure of structural similarity or diversity by their application to databases virtually generated by computer. In this article we will briefly review the history of TIs, their advantages and limitations with respect to other descriptors, and their possibilities in drug design and database selection. These applications rely on new computational techniques such as virtual combinatorial synthesis, virtual computational screening or inverse QSAR.

Use of molecular topology for the prediction of physico-chemical, pharmacokinetic and toxicological properties of a group of antihistaminic drugs.

We used molecular connectivity to search mathematical models for predicting physico-chemical (e.g. the partition coefficient, P), pharmacokinetic (e.g. the time of maximum plasma level, and toxicological properties (lethal dose, LD) for a group of antihistaminic drugs. The results obtained clearly reveal the high efficiency of molecular topology for the prediction of these properties. Randomization and cross-validation by use of leave-one-out tests were also performed in order to assess the stability and the prediction ability of the connectivity functions selected.

Prediction of quinolone activity against Mycobacterium avium by molecular topology and virtual computational screening.

We conducted a quantitative structure-activity relationship study using a database of 158 quinolones previously tested against Mycobacterium avium-M. intracellulare complex in order to develop a model capable of predicting the activity of new quinolones against the M. avium-M. intracellulare complex in vitro. Topological indices were used as structural descriptors and were related to anti-M. avium-M. intracellulare complex activity by using the linear discriminant analysis (LDA) statistical technique. The discriminant equation thus obtained correctly classified 137 of the 158 quinolones, including 37 of a test group of 44 randomly chosen compounds. This model was then applied to 24 quinolones, including recently developed fluoroquinolones, whose MICs were subsequently determined in vitro by using the Alamar blue microplate assay; the biological results confirmed the model's predictions. The MICs of these 24 quinolones were then treated by multilinear regression (MLR) to establish a model capable of classifying them according to their in vitro activities. Using this model, a good correlation between measured and predicted MICs was found (r(2) = 0.88; r(2)(cv) [cross-validation correlation] = 0.82). Moxifloxacin, sparfloxacin, and gatifloxacin were the most potent against the M. avium- M. intracellulare complex, with MICs of 0.2, 0.4, and 0.9 microg/ml, respectively. Finally, virtual modifications of these three drugs were evaluated in LDA and MLR models in order to determine the importance of different substituents in their activity. We conclude that the combination of molecular-topology methods, LDA, and MLR provides an excellent tool for the design of new quinolone structures with enhanced activity.

Anti-toxoplasma activities of 24 quinolones and fluoroquinolones in vitro: prediction of activity by molecular topology and virtual computational techniques.

The apicoplast, a plastid-like organelle of Toxoplasma gondii, is thought to be a unique drug target for quinolones. In this study, we assessed the in vitro activity of quinolones against T. gondii and developed new quantitative structure-activity relationship models able to predict this activity. The anti-Toxoplasma activities of 24 quinolones were examined by means of linear discriminant analysis (LDA) using topological indices as structural descriptors. In parallel, in vitro 50% inhibitory concentrations (IC(50)s) were determined in tissue culture. A multilinear regression (MLR) analysis was then performed to establish a model capable of classifying quinolones by in vitro activity. LDA and MLR analysis were applied to virtual structures to identify the influence of each atom or substituent of the quinolone ring on anti-Toxoplasma activity. LDA predicted that 20 of the 24 quinolones would be active against T. gondii. This was confirmed in vitro for most of the quinolones. Trovafloxacin, grepafloxacin, gatifloxacin, and moxifloxacin were the quinolones most potent against T. gondii, with IC(50)s of 0.4, 2.4, 4.1, and 5.1 mg/liter, respectively. Using MLR analysis, a good correlation was found between measured and predicted IC(50)s (r(2) = 0.87, cross-validation r(2) = 0.74). MLR analysis showed that the carboxylic group at position C-3 of the quinolone ring was not essential for anti-Toxoplasma activity. In contrast, activity was totally dependent on the presence of a fluorine at position C-6 and was enhanced by the presence of a methyl group at C-5 or an azabicyclohexane at C-7. A nucleophilic substituent at C-8 was essential for the activity of gatifloxacin and moxifloxacin.

Molecular search of new active drugs against Toxoplasma gondii.

Molecular connectivity has been applied to the search of new compounds with activity against the protozoan Toxoplasma gondii, using a stepwise linear discriminant analysis (SLDA) which is able to classify a compound according its activity either as active or as inactive. Among the selected compounds, andrographolide and dibenzotiophene sulfone stand out, both with IC50 values lower than 1 microgram/ml, which are comparable to these of drugs such as sulfamethoxazole, pyrimethamine and trimethoprim, with IC50 values equal to 1.1, 0.04 and 2.31 micrograms/ml, respectively. These results confirm the usefulness of our topological approach for the selection and design of new-lead drugs active against Toxoplasma gondii.

Discovery of new antimalarial compounds by use of molecular connectivity techniques.

Molecular connectivity has been applied to the search for new compounds with antimalarial activity. Linear discriminant analysis and connectivity functions were used to select several potentially suitable drugs which were tested for antimalarial properties by use of an in-vitro micro test which estimates parasite growth by measurement of incorporation of [3H]hypoxanthine. Hexetidine stands out among the compounds selected. Activity assays were performed with Plasmodium falciparum passou and 3CD7 strains, for which the IC50 values (doses resulting in 50% inhibition) were 320 and 400 ng mL(-1), respectively. These results are comparable with those obtained for quinine chlorhydrate (IC50 = 60 and 107.8 ng mL(-1)) and chloroquine sulphate (IC50 = 231 and 415 ng mL(-1)), the drugs used for reference. These results demonstrate the usefulness of our topological approach for the selection and design of new lead drugs active against Plasmodium falciparum.