Electronic structure of some adenosine receptor antagonists. VQSAR Investigation

Absorption spectra of xanthines in aqueous solution: a computational study

We present a detailed computational analysis of the UV/Vis spectra of caffeine, paraxanthine and theophylline in aqueous solution. A hierarchy of solvation approaches for modeling the aqueous environment have been tested, ranging from the continuum model to the non-polarizable and polarizable quantum mechanical (QM)/molecular mechanics (MM) models, with and without the explicit inclusion of water molecules in the QM portion. The computed results are directly compared with the experimental data, thus highlighting the role of electrostatic, polarization and hydrogen boding solute-solvent interactions.

Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

The use of antagonists for each adenosine receptor (AR) subtype as potent clinical candidates is of growing interest due to their involvement in the treatment of various diseases. The recent resolution of several A₁ and A_2A ARs X-ray structures provides opportunities for structure-based drug design. In this study, we describe the discovery of novel A₁AR antagonists by applying a multistage virtual screening approach, which is based on random forest (RF), e-pharmacophore modeling and docking methods. A multistage virtual screening approach was applied to screen the ChemDiv library (1,492,362 compounds). Among the final hits, 22 compounds were selected for further radioligand binding assay analysis against human A₁AR, and 18 compounds (81.82% success) exhibited nanomolar or low micromolar binding potency (K_i). Then, we selected six compounds (pK_i > 6) to further evaluate their antagonist profile in a cAMP functional assay, and we found that they had low micromolar antagonistic activity (pIC₅₀ = 5.51-6.38) for the A₁AR. Particularly, four of six compounds (pK_i > 6) showed very good affinity (pK_i = 6.11-7.13) and selectively (>100-fold) for A₁AR over A_2AAR. Moreover, the novelty analysis suggested that four of six compounds (pK_i > 6) were dissimilar to existing A₁AR antagonists and hence represented novel A₁AR antagonists. Further molecular docking and molecular dynamics (MD) studies showed that the three selective compounds 15, 20 and 22 were stabilized (RMS_lig value ≤ 2 Å) inside the binding pocket of A₁AR with similar orientations to the docking pose in 100-ns MD simulations, whereas they escaped from the binding area of A_2AAR with larger values of RMS_lig (RMS_lig ≥ 2 Å). We hope that these findings provide new insights into the discovery of drugs targeting A₁AR and facilitate research on new drugs and treatments for A₁AR-related human pathologies.

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

In view of the large libraries of nucleoside analogues that are now being handled in organic synthesis, the identification of drug biological activity is advisable prior to synthesis and this can be achieved by employing predictive biological property methods. In this sense, Quantitative Structure-Activity Relationships (QSAR) or docking approaches have emerged as promising tools. Although a large number of in silico approaches have been described in the literature for the prediction of different biological activities, the use of QSAR applications to develop adenosine receptor (AR) antagonists is not common as for the case of the antibiotics and anticancer compounds for instance. The intention of this review is to summarize the present knowledge concerning computational predictions of new molecules as adenosine receptor antagonists.

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.