Synthesis, molecular modeling studies and pharmacological activity of selective A1 receptor antagonists

We present a combined computational study aimed at identifying the three-dimensional structural properties required for different classes of compds. to show antagonistic activity toward the A1 adenosine receptor (AR). Particularly, an approach combining pharmacophore mapping, mol. alignment, and pseudoreceptor generation was applied to derive a hypothesis of the interaction pathway between a set of A1 AR antagonists taken from the literature and a model of the putative A1 receptor. The pharmacophore model consists of seven features and represents an improvement of the N6-C8 model, generally reported as the most probable pharmacophore model for A1 AR agonists and antagonists. It was used to build up a pseudoreceptor model able to rationalize the relationships between structural properties and biol. data of, and external to, the training set. In fact, to further assess its statistical significance and predictive power, the pseudoreceptor was employed to predict the free energy of binding assocd. with compds. constituting a test set. While part of these mols. was also taken from the literature, the remaining compds. were designed and synthesized by our research group. All of the new compds. were tested for their affinity toward A1, A2a, and A3 AR, showing interesting antagonistic activity and A1 selectivity.

We present a combined computational study aimed at identifying the three-dimensional structural properties required for different classes of compounds to show antagonistic activity toward the A1 adenosine receptor (AR). Particularly, an approach combining pharmacophore mapping, molecular alignment, and pseudoreceptor generation was applied to derive a hypothesis of the interaction pathway between a set of A1 AR antagonists taken from the literature and a model of the putative A1 receptor. The pharmacophore model consists of seven features and represents an improvement of the N6-C8 model, generally reported as the most probable pharmacophore model for A1 AR agonists and antagonists. It was used to build up a pseudoreceptor model able to rationalize the relationships between structural properties and biological data of, and external to, the training set. In fact, to further assess its statistical significance and predictive power, the pseudoreceptor was employed to predict the free energy of binding associated with compounds constituting a test set. While part of these molecules was also taken from the literature, the remaining compounds were designed and synthesized by our research group. All of the new compounds were tested for their affinity toward A1, A2a, and A3 AR, showing interesting antagonistic activity and A1 selectivity.