The Structural Determinants for α1-Adrenergic/Serotonin Receptors Activity among Phenylpiperazine-Hydantoin Derivatives

2-{[4-(4-Bromophenyl)piperazin-1-yl)]methyl}-4-(3-chlorophenyl)-5-(4-methoxyphenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione

The novel compound 2-{[4-(4-bromophenyl)piperazin-1-yl)]methyl}-4-(3-chlorophenyl-5-(4-methoxyphenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione is obtained in good yield via a three-step protocol. The product’s structure is assigned by HRMS, IR, 1H and 13C NMR experiments.

Synthesis, Docking Studies and Pharmacological Evaluation of Serotoninergic Ligands Containing a 5-Norbornene-2-Carboxamide Nucleus

A new series of 5-norbornene-2-carboxamide derivatives was prepared and their affinities to the 5-HT_1A, 5-HT_2A, and 5-HT_2C receptors were evaluated and compared to a previously synthesized series of derivatives characterized by exo-N-hydroxy-5-norbornene-2,3-dicarboximidenucleus, in order to identify selective ligands for the above-mentioned subtype receptors. Arylpiperazines represents one of the most important classes of 5-HT_1AR ligands, and recent research concerning new derivatives has been focused on the modification of one or more portions of such pharmacophore. The combination of structural elements (heterocyclic nucleus, propyl chain and 4-substituted piperazine), known to be critical to the affinity to 5-HT_1A receptors, and the proper selection of substituents led to compounds with high specificity and affinity towards serotoninergic receptors. The most active compounds were selected for further in vivo assays to determine their functional activity. Finally, to rationalize the obtained results, molecular docking studies were performed. The results of the pharmacological studies showed that Norbo-4 and Norbo-18 were the most active and promising derivatives for the serotonin receptor considered in this study.

A Concise and Useful Guide to Understand How Alpha1 Adrenoceptor Antagonists Work

Adrenoceptors are the receptors for the catecholamines, adrenaline and noradrenaline. They are divided in α (α1 and α2) and β (β1, β2 and β3). α1-Adrenoceptors are subdivided in α1A, α1B and α1D. Most tissues express mixtures of α1-adrenoceptors subtypes, which appear to coexist in different densities and ratios, and in most cases their responses are probably due to the activation of more than one type. The three subtypes of α1-adrenoceptors are G-protein-coupled receptors (GPCR), specifically coupled to Gq/11. Additionally, the activation of these receptors may activate other signaling pathways or different components of these pathways, which leads to a great variety of possible cellular effects. The first clinically used α1 antagonist was Prazosin, for Systemic Arterial Hypertension (SAH). It was followed by its congeners, Terazosin and Doxazosin. Nowadays, there are many classes of α-adrenergic antagonists with different selectivity profiles. In addition to SAH, the α1-adrenoceptors are used for the treatment of Benign Prostatic Hyperplasia (BPH) and urolithiasis. This antagonism may be part of the mechanism of action of tricyclic antidepressants. Moreover, the activation of these receptors may lead to adverse effects such as orthostatic hypotension, similar to what happens with the antidepressants and with some antipsychotic. Structure-activity relationships can explain, in part, how antagonists work and how selective they can be for each one of the subtypes. However, it is necessary to develop new molecules which antagonize the α1-adrenoceptors or make chemical modifications in these molecules to improve the selectivity, pharmacokinetic profile and/or reduce the adverse effects of known drugs.

From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output

An increasing number of crystal structures available on one side, and the boost of computational power available for computer-aided drug design tasks on the other, have caused that the structure-based drug design tools are intensively used in the drug development pipelines. Docking and molecular dynamics simulations, key representatives of the structure-based approaches, provide detailed information about the potential interaction of a ligand with a target receptor. However, at the same time, they require a three-dimensional structure of a protein and a relatively high amount of computational resources. Nowadays, as both docking and molecular dynamics are much more extensively used, the amount of data output from these procedures is also growing. Therefore, there are also more and more approaches that facilitate the analysis and interpretation of the results of structure-based tools. In this review, we will comprehensively summarize approaches for handling molecular dynamics simulations output. It will cover both statistical and machine-learning-based tools, as well as various forms of depiction of molecular dynamics output.