Comparative molecular field analysis (CoMFA) of a series of selective adenosine receptor A2A antagonists

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.

2-n-Butyl-9-methyl-8-[1,2,3]triazol-2-yl-9H-purin-6-ylamine and analogues as A2A adenosine receptor antagonists. Design, synthesis, and pharmacological characterization

Two types of adenosine receptor ligands were designed, i.e., 9H-purine and 1H-imidazo[4,5-c]pyridines, to obtain selective A(2A) antagonists, and we report here their synthesis and binding affinities for the four adenosine receptor subtypes A(1), A(2A), A(2B) and A(3). The design was carried out on the basis of the molecular modeling of a number of potent adenosine receptor antagonists described in the literature. Three compounds (25b-d) showed an interesting affinity and selectivity for the A(2A) subtype. One of them, i.e., ST1535 (2-n-butyl-9-methyl-8-[1,2,3]triazol-2-yl-9H-purin-6-ylamine, 25b) (K(i) A(2A) = 6.6 nM, K(i) A(1)/A(2A) = 12; K(i) A(2B)/A(2A) = 58; K(i) A(3)/A(2A) > 160), was selected for in vivo study and shown to induce a dose-related increase in locomotor activity, suggestive of an A(2A) antagonist type of activity.

Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+

Binding affinity data [Bioorg Med Chem (2004) 12:613-623] of thiazole and thiadiazole derivatives (n = 30) for the human adenosine A3 receptor subtype have been subjected to 3D-QSAR (Quantitative structure-activity relationships) analyses by molecular shape analysis (MSA) and molecular field analysis (MFA) techniques using Cerius2 Version 4.8. In the case of the MSA, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape-reference compound (based on the active conformation); (4) performing pairwise molecular superimposition using the maximum common subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA descriptors; (6) determining other molecular features by calculating spatial, electronic and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by genetic function algorithm (GFA) or stepwise regression. The best 3D-QSAR equation (MSA) obtained from GFA technique shows 70.0% predicted variance (leave-one-out) and 77.7% explained variance. This equation shows the importance of Jurs descriptors (atomic charge weighted positive surface area, relative negative charge and relative positive charge surface area), partial moment of inertia, energy of the most stable conformer and the ratio of common overlap steric volume to volume of individual molecules. In the case of stepwise regression, the best relation showed 46.1% predicted variance and 72.3% explained variance. In the case of MFA, the major steps were (1) generating conformers and energy minimization; (2) matching atoms using a maximum common substructure (MCS) search and aligning molecules using the default options; (3) setting MFA preferences (rectangular grid with 2 A step size, charges by the Gasteiger algorithm, H+ and CH3 as probes); (4) creating the field; (5) analysis by the Genetic partial least squares (G/PLS) method. The equation obtained was of excellent statistical quality: 96.1% explained variance and 71.6% predicted variance. Statistically reliable 3D-QSAR models obtained from this study suggest that these techniques could be useful to design potent A3 receptor antagonists.

QSAR of adenosine receptor antagonists. Part 3: Exploring physicochemical requirements for selective binding of 1,2,4-triazolo[5,1- i ]purine derivatives with human adenosine A 3 receptor subtype

Considering potential of selective adenosine A3 receptor antagonists in the development of prospective therapeutic agents, an attempt has been made to explore selectivity requirements of 1,2,4-triazolo[5,1-i]purine derivatives for binding with cloned human adenosine A3 receptor subtype. In this study, partition coefficient (logP) values of the molecules (calculated by Crippen's fragmentation method) and Wang-Ford charges of the common atoms of the triazolopurine nucleus (calculated from molecular electrostatic potential surface of energy minimized geometry using AM1 technique) were used as independent variables along with suitable dummy parameters. The best equation describing A3 binding affinity [n=29, Q2=0.796, Ra2=0.853, R2=0.874, R=0.935, s=0.342, F=41.5 (df 4,24), SDEP=0.396] showed parabolic relation with logP (optimum value being 4.134). Further, it was found that an aromatic substituent conjugated with the triazole nucleus should be present at R2 position for A3 binding affinity. Again, high negative charges on N2 and N4 are conducive to the binding affinity. While exploring selectivity requirements of the compounds for binding with A3 receptor over that with A2A receptor, the selectivity relation [n=23, Q2=0.909, Ra2=0.918, R2=0.933, R=0.966, s=0.401, F=62.4 (df 4,18), SDEP=0.412] showed that an aromatic R2 substituent conjugated with the triazole nucleus contributes significantly to the selectivity. Again, presence of a 4-substituted-phenyl ring (except 4-OH-phenyl and 4-CH3-phenyl) at R2 position also increases selectivity. Further, charge difference between N2 and N11 (negative charge on the former should be higher and that on the latter should be less) contributes significantly to the selectivity. In addition, negative charge on N7 is conducive while presence of substituents like propyl, butyl, pentyl or phenyl at R1 position is detrimental for the A3 selectivity.

Design, synthesis, and evaluation of novel A2A adenosine receptor agonists

We have been interested in the design, synthesis, and evaluation of novel adenosine A2A agonists. Through the use of comparative molecular field analysis (CoMFA) we have generated a training model that includes 78 structurally diverse A2A agonists and correlated their affinity for isolated rat brain receptors with differences in their structural and electrostatic properties. We validated this model by predicting the activity of a test set that included 24 additional A2A agonists. Our CoMFA model, which incorporates the physiochemical property of dipole and selects against A1 receptor activity, generated a correlated final model (r2 = 0.891) that provides for enhanced A2A selectivity and predictability. Synthesis, pharmacological evaluation, and modeling of four novel ligands further validate the utility and predictive power (r2 = 0.626) of the CoMFA model.