Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we?

Medicinal Chemistry and Therapeutic Potential of Agonists, Antagonists and Allosteric Modulators of A1 Adenosine Receptor: Current Status and Perspectives

Adenosine is a purine nucleoside, responsible for the regulation of a wide range of physiological and pathophysiological conditions by binding with four G-protein-coupled receptors (GPCRs), namely A1, A2A, A2B and A3 adenosine receptors (ARs). In particular, A1 AR is ubiquitously present, mediating a variety of physiological processes throughout the body, thus represents a promising drug target for the management of various pathological conditions. Agonists of A1 AR are found to be useful for the treatment of atrial arrhythmia, angina, type-2 diabetes, glaucoma, neuropathic pain, epilepsy, depression and Huntington's disease, whereas antagonists are being investigated for the treatment of diuresis, congestive heart failure, asthma, COPD, anxiety and dementia. However, treatment with full A1 AR agonists has been associated with numerous challenges like cardiovascular side effects, off-target activation as well as desensitization of A1 AR leading to tachyphylaxis. In this regard, partial agonists of A1 AR have been found to be beneficial in enhancing insulin sensitivity and subsequently reducing blood glucose level, while avoiding severe CVS side effects and tachyphylaxis. Allosteric enhancer of A1 AR is found to be potent for the treatment of neuropathic pain, culminating the side effects related to off-target tissue activation of A1 AR. This review provides an overview of the medicinal chemistry and therapeutic potential of various agonists/partial agonists, antagonists and allosteric modulators of A1 AR, with a particular emphasis on their current status and future perspectives in clinical settings.

Classifier ensemble based on feature selection and diversity measures for predicting the affinity of A(2B) adenosine receptor antagonists

A(2B) adenosine receptor antagonists may be beneficial in treating diseases like asthma, diabetes, diabetic retinopathy, and certain cancers. This has stimulated research for the development of potent ligands for this subtype, based on quantitative structure-affinity relationships. In this work, a new ensemble machine learning algorithm is proposed for classification and prediction of the ligand-binding affinity of A(2B) adenosine receptor antagonists. This algorithm is based on the training of different classifier models with multiple training sets (composed of the same compounds but represented by diverse features). The k-nearest neighbor, decision trees, neural networks, and support vector machines were used as single classifiers. To select the base classifiers for combining into the ensemble, several diversity measures were employed. The final multiclassifier prediction results were computed from the output obtained by using a combination of selected base classifiers output, by utilizing different mathematical functions including the following: majority vote, maximum and average probability. In this work, 10-fold cross- and external validation were used. The strategy led to the following results: i) the single classifiers, together with previous features selections, resulted in good overall accuracy, ii) a comparison between single classifiers, and their combinations in the multiclassifier model, showed that using our ensemble gave a better performance than the single classifier model, and iii) our multiclassifier model performed better than the most widely used multiclassifier models in the literature. The results and statistical analysis demonstrated the supremacy of our multiclassifier approach for predicting the affinity of A(2B) adenosine receptor antagonists, and it can be used to develop other QSAR models.

Revisiting a receptor-based pharmacophore hypothesis for human A(2A) adenosine receptor antagonists

The application of both structure- and ligand-based design approaches represents to date one of the most useful strategies in the discovery of new drug candidates. In the present paper, we investigated how the application of docking-driven conformational analysis can improve the predictive ability of 3D-QSAR statistical models. With the use of the crystallographic structure in complex with the high affinity antagonist ZM 241385 (4-(2-[7-amino-2-(2-furyl)[1,2,4]-triazolo[2,3-a][1,3,5]triazin-5-ylamino]ethyl)phenol), we revisited a general pharmacophore hypothesis for the human A(2A) adenosine receptor of a set of 751 known antagonists, by applying an integrated ligand- and structure-based approach. Our novel pharmacophore hypothesis has been validated by using an external test set of 29 newly synthesized human adenosine receptor antagonists.

Design, synthesis and biological evaluation of bivalent ligands against A(1)-D(1) receptor heteromers

AIM

To design and synthesize bivalent ligands for adenosine A1-dopamine D1 receptor heteromers (A1-D1R), and evaluate their pharmacological activities.

METHODS

Bivalent ligands and their corresponding A1R monovalent ligands were designed and synthesized. The affinities of the bivalent ligands for A1R and D1R in rat brain membrane preparation were examined using radiolabeled binding assays. To demonstrate the formation of A1-D1R, fluorescence resonance energy transfer (FRET) was conducted in HEK293 cells transfected with D1-CFP and A1-YFP. Molecular modeling was used to analyze the possible mode of protein-protein and protein-ligand interactions.

RESULTS

Two bivalent ligands for A1R and D1R (20a, 20b), as well as the corresponding A1R monovalent ligands (21a, 21b) were synthesized. In radiolabeled binding assays, the bivalent ligands showed affinities for A1R 10-100 times higher than those of the corresponding monovalent ligands. In FRET experiments, the bivalent ligands significantly increased the heterodimerization of A1R and D1R compared with the corresponding monovalent ligands. A heterodimer model with the interface of helixes 3, 4, 5 of A1R and helixes 1, 6, 7 from D1R was established with molecular modeling. The distance between the two ligand binding sites in the heterodimer model was approximately 48.4 Å, which was shorter than the length of the bivalent ligands.

CONCLUSION

This study demonstrates the existence of A1-D1R in situ and a simultaneous interaction of bivalent ligands with both the receptors.

The A3 adenosine receptor as multifaceted therapeutic target: pharmacology, medicinal chemistry, and in silico approaches

Adenosine is an ubiquitous local modulator that regulates various physiological and pathological functions by stimulating four membrane receptors, namely A(1), A(2A), A(2B), and A(3). Among these G protein-coupled receptors, the A(3) subtype is found mainly in the lung, liver, heart, eyes, and brain in our body. It has been associated with cerebroprotection and cardioprotection, as well as modulation of cellular growth upon its selective activation. On the other hand, its inhibition by selective antagonists has been reported to be potentially useful in the treatment of pathological conditions including glaucoma, inflammatory diseases, and cancer. In this review, we focused on the pharmacology and the therapeutic implications of the human (h)A(3) adenosine receptor (AR), together with an overview on the progress of hA(3) AR agonists, antagonists, allosteric modulators, and radioligands, as well as on the recent advances pertaining to the computational approaches (e.g., quantitative structure-activity relationships, homology modeling, molecular docking, and molecular dynamics simulations) applied to the modeling of hA(3) AR and drug design.