RESUMEN
Combined discriminant and regression analysis was carried out on a series of 167 A1 adenosine receptor agonists to identify the best linear and nonlinear models for the design of new compounds with a better biological profile. On the basis of the best linear discriminant analysis and both linear and nonlinear Multi Layer Perceptron neural networks regression, we have designed and synthesized 14 carbonucleoside analogues of adenosine. Their biological activities were predicted and experimentally measured to demonstrate the capability of our model to avoid the prediction of false positives. A good agreement was found between the calculated and observed biological activity.
Asunto(s)
Agonistas del Receptor de Adenosina A1 , Adenosina/análogos & derivados , Diseño de Fármacos , Relación Estructura-Actividad Cuantitativa , Animales , Análisis Discriminante , Humanos , Redes Neurales de la Computación , Análisis de RegresiónRESUMEN
The radial distribution function (RDF) approach has been applied to the study of the A(1) adenosine receptors agonist effect of 32 adenosine analogues. A model able to describe more than 79% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the three different approaches, including the use of 2D autocorrelations, BCUT and 3D-MORSE descriptors were able to explain more than 72% of the variance in the mentioned property with the same number of variables in the equation. In addition, we established a comparison with other models reported by us for this receptor subtype using this data set, and the RDF descriptors continue getting the best results.
Asunto(s)
Agonistas del Receptor de Adenosina A1 , Adenosina/análogos & derivados , Modelos Biológicos , Adenosina/química , Adenosina/farmacología , Animales , Relación Estructura-Actividad Cuantitativa , RatasRESUMEN
In order to minimize expensive drug failures it is essential to determine the potential biological activity of new candidates as early as possible. In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of a drugs biological activity is advisable even before synthesis and this can be achieved using predictive biological activity methods. In this sense, computer aided rational drug design strategies like Quantitative Structure Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of traditional QSAR applications in the development of new agonist molecules with affinity toward adenosine receptors is scarce. This review attempts to summarize the current level of knowledge concerning computational affinity predictions for adenosine receptors using QSAR models based on knowledge of the agonist ligands. Several computational protocols and different 2D and 3D descriptors have been described in the literature for these targets, but more effort is still required in this area.
Asunto(s)
Adenosina/análogos & derivados , Adenosina/uso terapéutico , Diseño de Fármacos , Agonistas del Receptor Purinérgico P1 , Relación Estructura-Actividad Cuantitativa , Adenosina/química , Humanos , Ligandos , Receptor de Adenosina A3/fisiología , Receptores Purinérgicos P1/fisiologíaRESUMEN
The BCUT descriptors have been applied to the study of the A(3) adenosine receptor agonist effect of 32 adenosine analogues. A model, able to describe more than 80% of the variance in the experimental activity was developed with the use of the above-mentioned approach. Four different approaches (topological, Galvez topological charges indexes, Randic molecular profiles, and geometrical descriptors) failed to give satisfactory models for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than BCUT, the best fitted model was still found with these descriptors.
Asunto(s)
Adenosina/farmacología , Técnicas Químicas Combinatorias , Receptor de Adenosina A3/metabolismo , Adenosina/análogos & derivados , Agonistas del Receptor de Adenosina A3 , Animales , Biología Computacional/métodos , Modelos Moleculares , Relación Estructura-Actividad Cuantitativa , Ratas , Receptor de Adenosina A3/químicaRESUMEN
The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the A1 adenosine receptors agonist effect of 32 adenosine analogues: N6-arylcarbamoyl, 2-arylalkynyl-N6-arylcarbamoyl, and N6-carboxamido derivatives. A model, able to describe more than 77% of the variance in the experimental activity, was developed with the use of the above mentioned approach. Five different approaches (Topological, Galvez Topological Charges indexes, Randic Molecular Profiles, Geometrical, and WHIM descriptors) failed to give satisfactory models (R2=0.70) for this property with the same number of variables in the equation. Although statistically significant models were derived containing descriptors other than GETAWAY, the best fitted out model was still found with these descriptors.
Asunto(s)
Agonistas del Receptor Purinérgico P1 , Modelos Moleculares , Relación Estructura-Actividad CuantitativaRESUMEN
The radial distribution function (RDF) approach has been applied to the study of the A(2B) agonist effect of a set of 89 adenosine analogues reported with this activity. A model able to describe more than 70% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the eleven different approaches including the use of Constitutional, Topological, Molecular walk count, BCUT, Galvez topological charge indices, 2D autocorrelations, Randic molecular profiles, Geometrical, 3D Morse, WHIM and GETAWAY descriptors was able to explain more than 47% of the variance in the mentioned property with the same number of descriptors.