Studies of new fused benzazepine as selective dopamine D3 receptor antagonists using 3D-QSAR, molecular docking and molecular dynamics

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum

We applied a novel molecular descriptor, three-dimensional biologically relevant spectrum (BRS-3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS-3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc-PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1-D2, D1-D3, D2-D3, D2-D4, and D3-D4. The models' 10-fold cross-validation-squared correlation coefficient (Q² , for training sets) and determination coefficient (R² , for test sets) were in the range of 0.5-0.7 and 0.6-0.8, respectively. Then, four pair-wise (D1-D2, D2-D3, D2-D4, and D3-D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS-3D and classical descriptors. The results showed that BRS-3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS-3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS-3D in the prediction of DR subtype-selective ligands.

Beyond small-molecule SAR: using the dopamine D3 receptor crystal structure to guide drug design

The dopamine D3 receptor is a target of pharmacotherapeutic interest in a variety of neurological disorders including schizophrenia, restless leg syndrome, and drug addiction. The high protein sequence homology between the D3 and D2 receptors has posed a challenge to developing D3 receptor-selective ligands whose behavioral actions can be attributed to D3 receptor engagement, in vivo. However, through primarily small-molecule structure-activity relationship (SAR) studies, a variety of chemical scaffolds have been discovered over the past two decades that have resulted in several D3 receptor-selective ligands with high affinity and in vivo activity. Nevertheless, viable clinical candidates remain limited. The recent determination of the high-resolution crystal structure of the D3 receptor has invigorated structure-based drug design, providing refinements to the molecular dynamic models and testable predictions about receptor-ligand interactions. This chapter will highlight recent preclinical and clinical studies demonstrating potential utility of D3 receptor-selective ligands in the treatment of addiction. In addition, new structure-based rational drug design strategies for D3 receptor-selective ligands that complement traditional small-molecule SAR to improve the selectivity and directed efficacy profiles are examined.

Insights into the structural biology of G-protein coupled receptors impacts drug design for central nervous system neurodegenerative processes

In the last few years, there have been important new insights into the structural biology of G-protein coupled receptors. It is now known that allosteric binding sites are involved in the affinity and selectivity of ligands for G-protein coupled receptors, and that signaling by these receptors involves both G-protein dependent and independent pathways. The present review outlines the physiological and pharmacological implications of this perspective for the design of new drugs to treat disorders of the central nervous system. Specifically, new possibilities are explored in relation to allosteric and orthosteric binding sites on dopamine receptors for the treatment of Parkinson's disease, and on muscarinic receptors for Alzheimer's disease. Future research can seek to identify ligands that can bind to more than one site on the same receptor, or simultaneously bind to two receptors and form a dimer. For example, the design of bivalent drugs that can reach homo/hetero-dimers of D2 dopamine receptor holds promise as a relevant therapeutic strategy for Parkinson's disease. Regarding the treatment of Alzheimer's disease, the design of dualsteric ligands for mono-oligomeric rinic receptors could increase therapeutic effectiveness by generating potent compounds that could activate more than one signaling pathway.

Investigation on quantitative structure activity relationships and pharmacophore modeling of a series of mGluR2 antagonists

MGluR2 is G protein-coupled receptor that is targeted for diseases like anxiety, depression, Parkinson’s disease and schizophrenia. Herein, we report the three-dimensional quantitative structure–activity relationship (3D-QSAR) studies of a series of 1,3-dihydrobenzo[ b][1,4]diazepin-2-one derivatives as mGluR2 antagonists. Two series of models using two different activities of the antagonists against rat mGluR2, which has been shown to be very similar to the human mGluR2, (activity I: inhibition of [³H]-LY354740; activity II: mGluR2 (1S,3R)-ACPD inhibition of forskolin stimulated cAMP.) were derived from datasets composed of 137 and 69 molecules respectively. For activity I study, the best predictive model obtained from CoMFA analysis yielded a Q² of 0.513, R²_ncv of 0.868, R²_pred = 0.876, while the CoMSIA model yielded a Q² of 0.450, R²_ncv = 0.899, R²_pred = 0.735. For activity II study, CoMFA model yielded statistics of Q² = 0.5, R²_ncv = 0.715, R²_pred = 0.723. These results prove the high predictability of the models. Furthermore, a combined analysis between the CoMFA, CoMSIA contour maps shows that: (1) Bulky substituents in R₇, R₃ and position A benefit activity I of the antagonists, but decrease it when projected in R₈ and position B; (2) Hydrophilic groups at position A and B increase both antagonistic activity I and II; (3) Electrostatic field plays an essential rule in the variance of activity II. In search for more potent mGluR2 antagonists, two pharmacophore models were developed separately for the two activities. The first model reveals six pharmacophoric features, namely an aromatic center, two hydrophobic centers, an H-donor atom, an H-acceptor atom and an H-donor site. The second model shares all features of the first one and has an additional acceptor site, a positive N and an aromatic center. These models can be used as guidance for the development of new mGluR2 antagonists of high activity and selectivity. This work is the first report on 3D-QSAR modeling of these mGluR2 antagonists. All the conclusions may lead to a better understanding of the mechanism of antagonism and be helpful in the design of new potent mGluR2 antagonists.