RESUMEN
Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.
Asunto(s)
Acridinas/química , Diseño Asistido por Computadora , Inhibidores Enzimáticos/química , Telomerasa/antagonistas & inhibidores , Concentración 50 Inhibidora , Modelos Moleculares , Estructura Molecular , Relación Estructura-Actividad Cuantitativa , Relación Estructura-ActividadRESUMEN
Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).
Asunto(s)
Botrytis/efectos de los fármacos , Fungicidas Industriales/síntesis química , Fungicidas Industriales/farmacología , Sesquiterpenos/química , Relación Estructura-ActividadRESUMEN
Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.
Asunto(s)
Carcinógenos/toxicidad , Compuestos Nitrosos/toxicidad , Relación Estructura-Actividad Cuantitativa , Algoritmos , Animales , Pruebas de Carcinogenicidad , Carcinógenos/administración & dosificación , Bases de Datos Factuales , Femenino , Intubación Gastrointestinal , Modelos Moleculares , Compuestos Nitrosos/administración & dosificación , RatasRESUMEN
The synthesis of D-mannosyl, D-galactosyl and D-glucosyl theophylline nucleosides by diethoxymethyl acetate (DEMA)-induced cyclization of 4-amino-5-glycosylideneimino-1,3-dimethyluracil is reported. 8-Methyltheophylline derivatives of the same sugars were also prepared by Ac(2)O/H(+)-induced cyclization of their imine precursors. This approach has allowed beta-D-mannopyranosyl-, alpha-D-galactofuranosyl- and beta-D-glucofuranosyltheophylline nucleosides to be synthesized for the first time. The inhibition of specific binding at A(1), A(2A), A(2B) and A(3) adenosine receptors in the mannose derivatives is also reported.
Asunto(s)
Nucleósidos/síntesis química , Antagonistas de Receptores Purinérgicos P1 , Teofilina/síntesis química , Uracilo/química , Acetatos/química , Unión Competitiva , Ciclización , Galactosa/análogos & derivados , Galactosa/síntesis química , Galactosa/química , Glucosa/análogos & derivados , Glucosa/síntesis química , Glucosa/química , Humanos , Iminas/química , Espectroscopía de Resonancia Magnética , Manosa/análogos & derivados , Manosa/síntesis química , Manosa/química , Estructura Molecular , Nucleósidos/química , Receptores Purinérgicos P1/química , Proteínas Recombinantes/química , Teofilina/químicaRESUMEN
Malaria is nowadays a worldwide and serious problem with a significant social, economic, and human cost, mainly in developing countries. In addition, the emergence and spread of resistance to existing antimalarial therapies deteriorate the global malaria situation, and lead thus to an urgent need toward the design and discovery of new antimalarial drugs. In this work, a QSAR predictive model based on GETAWAY descriptors was developed which is able to explain with, only three variables, more than 77% of the variance in antimalarial potency and displays a good internal predictive ability (of 73.3% and 72.9% from leave-one-out cross-validation and bootstrapping analyses, respectively). The performance of the proposed model was judged against other five methodologies providing evidence of the superiority of GETAWAY descriptors in predicting the antimalarial potency of the bisbenzamidine family. Moreover, a desirability analysis based on the final QSAR model showed that to be a useful way of selecting the predictive variable level necessary to obtain potent bisbenzamidines. From the proposed model it is also possible to infer that elevated high atomic masses/polarizabilities/van der Waals volumes could play a negative/positive/positive role in the molecular interactions responsible for the desired drug conformation, which is required for the optimal binding to the macromolecular target. The results obtained point out that our final QSAR model is statistically significant and robust as well as possessing a high predictive effectiveness. Thus, the model provides a feasible and practical tool for looking for new and potent antimalarial bisbenzamidines.
Asunto(s)
Antimaláricos/química , Antimaláricos/farmacología , Pentamidina/química , Pentamidina/farmacología , Simulación por Computador , Modelos Moleculares , Estructura Molecular , Relación Estructura-Actividad CuantitativaRESUMEN
MARCH-INSIDE methodology was applied to the prediction of the bitter tasting threshold of 48 dipeptides by means of pattern recognition techniques, in this case linear discriminant analysis (LDA), and regression methods. The LDA models yielded a percentage of good classification higher than 80% with the two main families of descriptor generated by this methodology (95.8% for self return probability and 83.3% using electronic delocalization entropy). The regression models can explain more than 80% of the experimental variance of the independent variable. Two regression models were obtained with R(2) values of 0.82 and 0.88 for the whole data and the data without two outliers, respectively; having a standard deviation of 0.27 and 0.23. The predictive power of the obtained equations was assessed by the Leave-One-Out cross validation procedures, giving the same percentages of good classification as in the training set, in the LDA models, and yielding values of q(2) of 0.78 and 0.86 in the regression model, respectively. The validation of this methodology was also carried out by comparison with previous reports modeling this data with other well-known methodologies, even 3-D molecular descriptors.
Asunto(s)
Dipéptidos/fisiología , Modelos Biológicos , Umbral Gustativo/fisiología , Procesos Estocásticos , Umbral Gustativo/efectos de los fármacosRESUMEN
A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in anti-inflammatory compounds using computer-aided molecular design. Two series of compounds, one containing anti-inflammatory and the other containing nonanti-inflammatory compounds were processed by a k-means cluster analysis in order to design the training and prediction sets. A linear classification function to discriminate the anti-inflammatory from the inactive compounds was developed. The model correctly and clearly classified 88% of active and 91% of inactive compounds in the training set. More specifically, the model showed a good global classification of 90%, that is, (399 cases out of 441). While in the prediction set, they showed an overall predictability of 88% and 84% for active and inactive compounds, being the global percentage of good classification of 85%. Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments towards anti-inflammatory property, also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general 'in silico' technique to experimentation in anti-inflammatory discovery.
Asunto(s)
Antiinflamatorios/química , Diseño Asistido por Computadora , Diseño de Fármacos , Relación Estructura-Actividad Cuantitativa , Antiinflamatorios/farmacología , Análisis por ConglomeradosRESUMEN
The TOPological Sub-Structural Molecular Design (TOPS-MODE) approach has been applied to the study of the affinity of A(1) adenosine receptor of different N(6)-(substituted-phenylcarbamoyl) adenosine-5'-uronamides analogues. A model able to describe close to 84% of the variance in the values for binding experiments of 23 analogues of these compounds through multilinear regression analysis (MRA) was developed with the use of the mentioned approach. In contrast, no one of three different approaches, with the same number of variables, including the use of BCUT, randic molecular profiles, and geometrical descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted us to find the contribution of different fragments to the biological property giving the model a straightforward structural interpretability.
Asunto(s)
Agonistas del Receptor de Adenosina A1 , Adenosina/análogos & derivados , Adenosina/farmacología , Modelos Biológicos , Amidas/química , Amidas/farmacología , Animales , Modelos Químicos , Relación Estructura-Actividad Cuantitativa , Ratas , Análisis de Regresión , Ácidos Urónicos/química , Ácidos Urónicos/farmacologíaRESUMEN
A new application of TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was carried out in herbicides using computer-aided molecular design. Two series of compounds, one containing herbicide and the other containing nonherbicide compounds, were processed by a k-Means Cluster Analysis in order to design the training and prediction sets. A linear classification function to discriminate the herbicides from the nonherbicide compounds was developed. The model correctly and clearly classified 88% of active and 94% of inactive compounds in the training set. More specifically, the model showed a good global classification of 91%, i.e., (168 cases out of 185). While in the prediction set, they showed an overall predictability of 91% and 92% for active and inactive compounds, being the global percentage of good classification of 92%. To assess the range of model applicability, a virtual screening of structurally heterogeneous series of herbicidal compounds was carried out. Two hundred eighty-four out of 332 were correctly classified (86%). Furthermore this paper describes a fragment analysis in order to determine the contribution of several fragments toward herbicidal property; also the present of halogens in the selected fragments were analyzed. It seems that the present TOPS-MODE based QSAR is the first alternate general "in silico" technique to experimentation in herbicides discovery.
Asunto(s)
Diseño Asistido por Computadora , Diseño de Fármacos , Herbicidas/química , Análisis por Conglomerados , Bases de Datos Factuales , Modelos Químicos , Compuestos Orgánicos/química , Compuestos Orgánicos/clasificación , Compuestos Orgánicos/farmacología , Relación Estructura-Actividad CuantitativaRESUMEN
The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.