1,3-Dialkyl-8-(hetero)aryl-9-OH-9-deazaxanthines as potent A2B adenosine receptor antagonists: Design, synthesis, structure–affinity and structure–selectivity relationships

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.

Synthesis and hypoglycemic activity of some new theophylline derivatives

Thirty-one new theophylline derivatives have been synthesized and evaluated for their hypoglycemic activity. Compounds 24 (56% reduction) and 31 (57% reduction) showed better hypoglycemic activity than the standard drug glibenclamide which showed 52% reduction in serum glucose level. Compound 27 remarkably reduced serum glucose level by 53%. Ten compounds showed varying degrees of hypoglycemic activity ranging from 20 to 37% reduction in serum glucose level compared to the standard drug. The aromatic amide functionality is the common feature of these theophylline hypoglycemic derivatives. However, anthranilamide and or aliphatic amides proved to be the least active compounds in the present series.

Synthesis, Characterisation and Bioactivity of Polysubstituted 1-(4-(1H-Pyrrol-1-yl)Phenyl)-1H-Pyrrole Derivatives

1,4-Phenylenediamine reacted readily with chloroacetone to give 1,4-bis[(2-oxopropyl)amino]benzene which was used to prepare 1-(4-(1H-pyrrol-1-yl)phenyl)-1H-pyrrole derivatives in a one pot reaction with dimethylformamide dimethylacetal or triethyl orthoformate and an active methylene nitrile, an active methylene ketone or an ylidene-malononitrile. Reaction of 1,4-bis[(2-oxopropyl)amino]benzene with arene diazonium salts afforded the hydrazone derivatives which readily cyclised when reacted with malononitrile to give bispyrrole derivatives. The antibacterial activity of some of the products was determined.

Structure based prediction of subtype-selectivity for adenosine receptor antagonists

One of the major hurdles in the development of safe and effective drugs targeting G-protein coupled receptors (GPCRs) is finding ligands that are highly selective for a specific receptor subtype. Structural understanding of subtype-specific binding pocket variations and ligand-receptor interactions may greatly facilitate design of selective ligands. To gain insights into the structural basis of ligand subtype selectivity within the family of adenosine receptors (AR: A(1), A(2A), A(2B), and A(3)) we generated 3D models of all four subtypes using the recently determined crystal structure of the A(A2)AR as a template, and employing the methodology of ligand-guided receptor optimization for refinement. This approach produced 3D conformational models of AR subtypes that effectively explain binding modes and subtype selectivity for a diverse set of known AR antagonists. Analysis of the subtype-specific ligand-receptor interactions allowed identification of the major determinants of ligand selectivity, which may facilitate discovery of more efficient drug candidates.

Synthesis and pharmacological evaluation of novel 1,3,8- and 1,3,7,8-substituted xanthines as adenosine receptor antagonists

A number of novel xanthines bearing a variety of substituents at positions 1, 3, 7 and 8 were prepared and evaluated for their binding affinity to the human adenosine receptor A(1), A(2A), A(2B) and A(3) subtypes. Several of the 1,3,8- and 1,3,7,8-substituted xanthines showed moderate-to-high affinity at human A(2B) and A(1) receptors, with the most active compound (14q) having a pK(i) of 7.57 nM for hA(2B) receptors and a selectivity over hA(2A) receptors of 8.1-fold and hA(1) receptors of 3.7-fold.

Recent developments in A2B adenosine receptor ligands

A selective, high-affinity A(2B) adenosine receptor (AR) antagonist will be useful as a pharmacological tool to help determine the role of the A(2B)AR in inflammatory diseases and angiogenic diseases. Based on early A(2B)AR-selective ligands with nonoptimal pharmaceutical properties, such as 15 (MRS 1754: K(i)(hA(2B)) = 2 nM; K(i)(hA(1)) = 403 nM; K(i)(hA(2A)) = 503 NM, and K(i)(hA(3)) = 570 nM), several groups have discovered second-generation A(2B)AR ligands that are suitable for development. Scientists at CV Therapeutics have discovered the selective, high-affinity A(2B)AR antagonist 22, a 8-(4-pyrazolyl)-xanthine derivative, (CVT-6883, K(i)(hA(2B)) = 22 nM; K(i)(hA(1)) = 1,940 nM; K(i)(hA(2A)) = 3,280; and K(i)(hA(3)) = 1,070 nM). Compound 22 has demonstrated favorable pharmacokinetic (PK) properties (T(1/2) = 4 h and F > 35% rat), and it is a functional antagonist at the A(2B)AR(K (B) = 6 nM). In a mouse model of asthma, compound 22 demonstrated a dose-dependent efficacy supporting the role of the A(2B)AR in asthma. In two Phase I clinical trails, 22 (CVT-6883) was found to be safe, well tolerated, and suitable for once-daily dosing. Baraldi et al. have independently discovered a selective, high-affinity A(2B)AR antagonist, 30 (MRE2029F20), 8-(5-pyrazolyl)-xanthine (K(i)(hA(2B)) = 5.5 nM; K(i)(hA(1)) = 200 nM; K(i)(hA(2A), A(3)) > 1,000, that has been selected for development in conjunction with King Pharmaceuticals. Compound 30 has been demonstrated to be a functional antagonist of the A(2B)AR, and it has been radiolabeled for use in pharmacological studies. A third compound, 58 (LAS-38096), is a 2-aminopyrimidine derivative (discovered by the Almirall group) that has high A(2B)AR affinity and selectivity (K(i)(hA(2B)) = 17 nM; K(i)(hA(1)) > 1,000 nM; K(i)(hA(2A)) > 2,500; and K(i)(hA(3)) > 1,000 nM), and 58 has been moved into preclinical safety testing. A fourth selective, high-affinity A(2B)AR antagonist, 54 (OSIP339391 K(i))(hA(2B)) = 0.5 nM; K(i))(hA(1)) = 37 nM; K(i))(hA(2A)) = 328; and K(i))(hA(3)) = 450 nm) was discovered by the OSI group. The three highly selective, high-affinity A(2B)AR antagonists that have been selected for development should prove useful in subsequent clinical trials that will establish the role of the A(2B)ARs in various disease states.