Gene knockout approach to adenosine A2A receptors in Parkinson's disease

Exploring QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists using FA and GFA techniques

Binding affinity data of thiazole and thiadiazole derivatives (n=30) for human adenosine A3 receptor subtype have been subjected to Quantitative Structure-Activity Relationship (QSAR) analysis using quantum chemical and hydrophobicity parameters. Wang-Ford charges of the common atoms of the compounds [calculated from molecular electrostatic potential surface of energy minimized geometry using Austin Model 1 (AM1) technique] were used as independent variables apart from partition coefficient (logP) and suitable dummy parameters. The variables for the multiple regression analyses were selected based on principal component factor analysis (FA), and generated equations were statistically validated using leave-one-out technique. The best equation thus obtained explained and predicted 74.4% and 68.9% respectively of the variance of the binding affinity. The results suggested importance of Wang-Ford charges of atoms C2, C5 and C7. Furthermore, the A3 binding affinity increases with decrease of lipophilicity of the compounds and in the presence of methyl or ethyl substituent at R position. Again, the binding affinity decreases in the presence of tert-butyloxy group at R position. When factor scores were used as predictor variables in principal component regression analysis, the resulted model showed 87.0% predicted variance and 89.5% explained variance. The data set was also modeled using genetic function approximation (GFA) technique. The best two equations derived from GFA show better predicted variance values (0.753 and 0.739) than that found in case of the best equation derived from FA. However, considerable intercorrelation was found between two predictor variables in case of GFA derived equations. GFA derived equations show importance of Wang-Ford charges of different atoms of the thiazole/thiadiazole nucleus and phenyl ring (S9, X8 and C2, the effects of the first two being predominant) along with similar impact of lipophilicity and R group on the binding affinity as found in case of the FA derived relation.

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.