Alpha(1) adrenoceptor subtype selectivity. 3D-QSAR models for a new class of alpha(1) adrenoceptor antagonists derived from the novel antipsychotic sertindole

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.

Discovery of novel α₁-adrenoceptor ligands based on the antipsychotic sertindole suitable for labeling as PET ligands

The synthesis and in vitro preclinical profile of a series of 5-heteroaryl substituted analogs of the antipsychotic drug sertindole are presented. Compounds 1-(4-fluorophenyl)-3-(1-methylpiperidin-4-yl)-5-(pyrimidin-5-yl)-1H-indole (Lu AA27122, 3i) and 1-(4-fluorophenyl)-5-(1-methyl-1H-1,2,4-triazol-3-yl)-3-(1-methylpiperidin-4-yl)-1H-indole (3l) were identified as high affinity α(1A)-adrenoceptor ligands with K(i) values of 0.52 and 0.16 nM, respectively, and with a >100-fold selectivity versus dopamine D(2) receptors. Compound 3i showed almost equal affinity for α(1B)- (K(i)=1.9 nM) and α(1D)-adrenoceptors (K(i)=2.5 nM) as for α(1A), as well as moderate affinity for 5-HT(1B) (K(i)=13 nM) and 5-HT(6) (K(i)=16 nM) receptors, whereas 3l showed >40-fold selectivity toward all other targets tested. Based on in vitro assays for assessment of permeability rates and extent, it is predicted that both compounds enter the brain of rats, non-human primates, as well as humans, and as such are good candidates to be carried forward for further evaluation as positron emission tomography (PET) ligands.

Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) studies on α(1A)-adrenergic receptor antagonists based on pharmacophore molecular alignment

The α_1A-adrenergic receptor (α_1A-AR) antagonist is useful in treating benign prostatic hyperplasia, lower urinary tract symptoms, and cardiac arrhythmia. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies were performed on a set of α_1A-AR antagonists of N-aryl and N-nitrogen class. Statistically significant models constructed from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were established based on a training set of 32 ligands using pharmacophore-based molecular alignment. The leave-oneout cross-validation correlation coefficients were q²_CoMFA = 0.840 and q²_CoMSIA = 0.840. The high correlation between the cross-validated/predicted and experimental activities of a test set of 12 ligands revealed that the CoMFA and CoMSIA models were robust (r²_pred/CoMFA = 0.694; r²_pred/CoMSIA = 0.671). The generated models suggested that electrostatic, hydrophobic, and hydrogen bonding interactions play important roles between ligands and receptors in the active site. Our study serves as a guide for further experimental investigations on the synthesis of new compounds. Structural modifications based on the present 3D-QSAR results may lead to the discovery of other α_1A-AR antagonists.

QSAR-CoMSIA applied to antipsychotic drugs with their dopamine D2 and serotonine 5HT2A membrane receptors

Antipsychotic drugs are psychiatric medication primarily used to manage psychosis (e.g., delusions or hallucinations), particularly in schizophrenia and bipolar disorder. First and second generations of antipshychotics tend to block receptors in the brain's dopamine pathways, but antipsychotic drugs encompass a wide range of receptor targets. The inhibition constant, Ki, at the level of membrane receptors is a major determinant of their pharmacokinetic behavior and, consequently, it can affect their antipsychotic activity. Here, predicted inhibition constants, Ki for 71 antipsychotics, already approved for clinical treatment, as well as representative new chemical structures which exhibit antipsychotic activity, were evaluated using 3D-QSAR-CoMSIA models. Significant values of the cross-validated correlation q2 (higher than 0.70) and the fitted correlation r2 (higher than 0.80) revealed that these models have reasonable power to predict the biological affinity of the 15 new risperidone and 12 new olanzapine derivatives in interactions with dopamine D2 and serotonin 5HT2A receptors; these compounds are suggested for further studies.

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.

Range and Sensitivity as Descriptors of Molecular Property Spaces in Dynamic QSAR Analyses

In this paper, we report the first study aimed at correlating pharmacological properties with molecular parameters derived from the physicochemical property space of bioactive molecules. A dataset of 36 ligands of the alpha(1a)-, alpha(1b)-, and alpha(1d)-adrenoceptors as published by Bremner et al. (Bioorg. Med. Chem. 2000, 8, 201-214) was used. One thousand conformers were generated for each ligand by Monte Carlo conformational analysis, and four 3D-dependent physicochemical properties were computed for each conformer of each ligand, namely virtual lipophilicity (log P), dipole moment, polar surface area (PSA), and solvent-accessible surface area (SAS). Thus, a space of four physicochemical properties was obtained for each ligand. These spaces were assessed by two descriptors, namely their range and their sensitivity (i.e., the variation amplitude of a given physicochemical property for a given variation in molecular geometric properties). Little or no correlation was found to exist between the physicochemical properties and their range or sensitivity, indicating that the latter descriptors do not encode the same molecular information as the former properties. As expected, neither the range nor the sensitivity of any of the four physicochemical properties correlated with receptor affinities. In contrast, range and sensitivity showed promising correlations with deltapK(a-b) (i.e., the alpha(1a)/alpha(1b) selectivity) for the complete dataset. The correlations were lower for deltapK(a-d) (i.e., the alpha(1a)/alpha(1d) selectivity), whereas there was no correlation at all with deltapK(b-d). These results are consistent with the results of Bremner et al., which indicate that the alpha(1a)-AR ligands bind in an extended geometry, whereas the alpha(1b)-AR and alpha(1d)-AR ligands assume more folded conformations. Since the property space descriptors presented here take structural variability into account, their correlation with deltapK(a-b) and deltapK(a-d) indicates that these selectivities are indeed driven by differences in conformational behavior and hence in property spaces.

New α1-adrenoceptor antagonists derived from the antipsychotic sertindole - carbon-11 labelling and pet examination of brain uptake in the cynomolgus monkey

Central alpha(1)-adrenergic receptors are potential targets for recently developed antipsychotic drugs. Two new 11C labeled potent and selective alpha(1)-adrenoceptor antagonists, 1- [2- [4-[1-(4-fluorophenyl)-5-(2-[(11)C]methyl-tetrazol-5-yl)-1H-indol-3-yl]-1-piperidinyl]ethyl]-imidazolidin-2-one ([(11)C]2) and 1- [2- [4-[1-(4-fluorophenyl)-5-(1-[(11)C]methyl-(1,2,3-triazol-4-yl)-1H-indol-3-yl]-1-piperidinyl]ethyl]-imidazolidin-2-one ([(11)C]3) were prepared and evaluated for imaging of central alpha(1)-adrenergic receptors in the cynomolgus monkey brain. For both compounds, the total brain radioactivity was only about 0.6% of the radioactivity injected i.v. There was no evident binding in regions known to contain alpha(1)-adrenoceptors. This observation suggests that the affinity of the radioligands in primates in vivo is not sufficient to provide a signal for specific binding that can be differentiated from the background. In addition, active efflux by P-glycoprotein may be responsible for the low total brain-uptake of the two radioligands. Both compounds showed a highly polarised and verapamile sensitive transport across monolayers of Caco-2 cells. The total brain-uptake of [(3)H]2 was 6 times higher in mdr1a(-/-) knock-out mice lacking the gene encoding P-glycoprotein compared to wild type mice. Pretreatment of one monkey with Cyclosporin A (15 mg/kg) resulted in 40% higher brain uptake for [(11)C]3 when compared with baseline. These observations support the view that efflux by P-glycoprotein can be of quantitative importance for the total brain-uptake of some PET radioligands.