Relevance of theoretical molecular descriptors in quantitative structure-activity relationship analysis of alpha1-adrenergic receptor antagonists

Exploring Translocator Protein (TSPO) Medicinal Chemistry: An Approach for Targeting Radionuclides and Boron Atoms to Mitochondria

Translocator protein 18 kDa [TSPO or peripheral-type benzodiazepine receptor (PBR)] was identified in the search of binding sites for benzodiazepine anxiolytic drugs in peripheral regions. In these areas, binding sites for TSPO ligands were recognized in steroid-producing tissues. TSPO plays an important role in many cellular functions, and its coding sequence is highly conserved across species. TSPO is located predominantly on the membrane of mitochondria and is overexpressed in several solid cancers. TSPO basal expression in the CNS is low, but it becomes high in neurodegenerative conditions. Thus, TSPO constitutes not only as an outstanding drug target but also as a valuable marker for the diagnosis of a number of diseases. The aim of the present article is to show the lesson we have learned from our activity in TSPO medicinal chemistry and in approaching the targeted delivery to mitochondria by means of TSPO ligands.

In Silico Target Prediction for Small Molecules

Drugs modulate disease states through their actions on targets in the body. Determining these targets aids the focused development of new treatments, and helps to better characterize those already employed. One means of accomplishing this is through the deployment of in silico methodologies, harnessing computational analytical and predictive power to produce educated hypotheses for experimental verification. Here, we provide an overview of the current state of the art, describe some of the well-established methods in detail, and reflect on how they, and emerging technologies promoting the incorporation of complex and heterogeneous data-sets, can be employed to improve our understanding of (poly)pharmacology.

Synthesis and biological evaluation of N-arylpiperazine derivatives of 4,4-dimethylisoquinoline-1,3(2H,4H)-dione as potential antiplatelet agents

Despite the substantial clinical success of aspirin and clopidogrel in secondary prevention of ischemic stroke, up to 40% of patients remain resistant to the available antiplatelet treatment. Therefore, there is an urgent clinical need to develop novel antiplatelet agents with a novel mechanism of action. Recent studies revealed that potent alpha 2B-adrenergic receptor (alpha 2B-ARs) antagonists could constitute alternative antiplatelet therapy. We have synthesized a series of N-arylpiperazine derivatives of 4,4-dimethylisoquinoline-1,3(2H,4H)-dione as potential alpha 2B receptor antagonists. The most potent compound 3, effectively inhibited the platelet-aggregation induced both by collagen and ADP/adrenaline with IC₅₀ of 26.9 μM and 20.5 μM respectively. Our study confirmed that the alpha 2B-AR antagonists remain an interesting target for the development of novel antiplatelet agents with an alternative mechanism of action.

Ligand-based pharmacophore model of N-Aryl and N-Heteroaryl piperazine alpha 1A-adrenoceptors antagonists using GALAHAD

Computer aided drug discovery for selective antagonism effects on alpha(1A) subtypes of G-protein coupled receptors are important in the treatment of benign prostatic hyperplasia (BPH). Ligand-based pharmacophore models of N-Aryl and N-Heteroaryl piperazine alpha(1A)-antagonists were developed using two separate training sets. Pharmacophore models were generated using the flexible align method within the GALAHAD module, implemented in SYBYL8.1 software. The most significant pharmacophore hypothesis, characterized by the conflicting demands of maximizing pharmacophore consensus, maximizing steric consensus, and minimizing energy, consisted of one positive nitrogen center, one donor atom center, two acceptor atom centers, and two hydrophobic groups. The most active compound in each class training set showed a good fit with all features of the pharmacophore proposed. The resulting models also had something in common with the hypothesis using the Catalyst software reported in other publications. These alpha(1A) pharmacophore models could predict compounds well, both in the training set and the test set. The pharmacophore models were also validated by an external dataset using a portion of the ZINC database. A 3D-QSAR model using the pharmacophore model to align the compounds was established in this study. The CoMFA model with the cross-validated q(2) value of 0.735 revealed that the model was valid. Our research provides a valuable tool for designing new therapeutic compounds with desired biological activity.

A Genetic-Function-Approximation-Based QSAR Model for the Affinity of Arylpiperazines toward α1 Adrenoceptors

The genetic function approximation (GFA) algorithm has been used to derive a three-term QSAR equation able to correlate the structural properties of arylpiperazine derivatives with their affinity toward the alpha1 adrenoceptor (alpha1-AR). The number of rotatable bonds, the hydrogen-bond properties, and a variable belonging to a topological family of descriptors (chi) showed significant roles in the binding process toward alpha1-AR. The new model was also compared to a previous pharmacophore for alpha1-AR antagonists and a QSAR model for alpha2-AR antagonists with the aim of finding common or different key determinants influencing both affinity and selectivity toward alpha1- and alpha2-AR.

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.

Theoretical quantitative structure–activity relationships of flavone ligands interacting with cytochrome P450 1A1 and 1A2 isozymes

Theoretical descriptors obtained from quantum mechanical calculations on isolated ligands in different media and molecular dynamics simulations of ligand-enzyme complexes have been used to obtain a quantitative rationalization of the inhibition of CYP1A2 and CYP1A2 by three series of flavonoids. Predictive models obtained through one-descriptor QSAR studies and mechanistic explanations have been obtained for recognition and selectivity.

Pharmacophore identification of α1A-adrenoceptor antagonists

A chemical feature based pharmacophore model was developed for alpha(1A)-adrenoceptor antagonists by HypoGen module implemented in catalyst software package. The best scoring pharmacophore hypothesis, Hypo1, consisted of four important chemical features (one positive ion, one hydrogen-bond donor, one aromatic ring, and one hydrophobic group). The results of our study provide a valuable tool in designing new leads with desired biological activity by virtual screening.

Prediction of selectivity of α1-adrenergic antagonists by counterpropagation neural network (CP-ANN)

A quantitative structure-selectivity relationships of series of structurally diverse alpha1-adrenergic antagonists was performed by using counter-propagation neural network (CP-ANN). The theoretical molecular descriptors have been calculated and selected using CODESSA program. The results obtained for a highly non-congeneric set of molecules have confirmed the potential of use of CP-ANN approach in prediction of relative activity (selectivity) of alpha1-adrenergic antagonists.

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

Receptor-binding affinities for the alpha(1) adrenoceptor subtypes alpha(1a), alpha(1b) and alpha(1d) for a series of 39 alpha(1) adrenoceptor antagonists derived from the antipsychotic sertindole are reported. The SAR of the compounds with respect to affinity for the alpha(1a), alpha(1b) and alpha(1d) adrenoceptor subtypes as well as affinity obtained by an alpha(1) assay (rat brain membranes) were investigated using a 3D-QSAR approach based on the GRID/GOLPE methodology. Good statistics (r(2)=0.91-0.96; q(2)=0.65-0.73) were obtained with the combination of the water (OH2) and methyl (C3) probes. The combination of steric repulsion and electrostatic attractions explain the affinities of the included molecules. The adrenergic alpha(1a) receptor seems to be more tolerant to large substituents in the area between the indole 5- and 6-positions compared to the adrenergic alpha(1b) and alpha(1d) receptor subtypes. There seems to be minor differences in the position of areas in the alpha(1b) receptor compared to alpha(1a) and alpha(1d) receptors where electrostatic interaction between the molecules and the receptor (OH2 probe) contribute to increased affinity. These observations may be used in the design of new subtype selective compounds. In addition, the model based on biological data from an alpha(1) assay (rat brain membranes) resembles the model for the alpha(1b) adrenoceptor subtype.

Novel potent 5-HT(3) receptor ligands based on the pyrrolidone structure: synthesis, biological evaluation, and computational rationalization of the ligand-receptor interaction modalities

Novel conformationally constrained derivatives of classical 5-HT(3) receptor antagonists were designed and synthesized with the aim of probing the central 5-HT(3) receptor recognition site in a systematic way. The newly-synthesized compounds were tested for their potential ability to inhibit [(3)H]granisetron specific binding to 5-HT(3) receptor in rat cortical membranes. These studies revealed subnanomolar affinity in some of the compounds under study. The most potent ligand in this series was found to be quinuclidine derivative (S)-7i, which showed an affinity comparable with that of the reference ligand granisetron. The potential 5-HT(3) agonist/antagonist activity of some selected compounds was assessed in vitro on the 5-HT(3) receptor-dependent [(14)C]guanidinium uptake in NG 108-15 cells. Both of the tropane derivatives tested in this functional assay (7a and 9a) showed antagonist properties, while the quinuclidine derivatives studied [the enantiomers of compounds 7i, 8g, and 9g, and compound (R)-8h] showed a full range of intrinsic efficacies. Therefore, the functional behavior of these 5-HT(3) receptor ligands appears to be affected by the structural features of both the azabicyclo moiety and the heteroaromatic portion. In agreement with the data obtained on NG 108-15 cells, investigations on the 5-HT(3) receptor-dependent Bezold-Jarisch reflex in urethane-anaesthetized rats confirmed the 5-HT(3) receptor antagonist properties of compounds 7a and (S)-7i showing for these compounds ID(50) values of 2.8 and 181 microg/kg, respectively. Finally, compounds 7a, (S)-7i and 9a (at the doses of 0.01, 1.0, and 0.01 mg/kg ip, respectively) prevented scopolamine-induced amnesia in the mouse passive avoidance test suggestive of a potential usefulness in cognitive disorders for these compounds. Qualitative and quantitative structure-affinity relationship studies were carried out by means of theoretical descriptors derived on a single structure and ad-hoc defined size and shape descriptors (indirect approach). The results showed to be useful in capturing information relevant to ligand-receptor interaction. Additional information derived by the analysis of the energy minimized 3-D structures of the ligand-receptor complexes (direct approach) suggested interesting mechanistic and methodological considerations on the binding mode multiplicity at the 5-HT(3) receptors and on the degree of tolerance allowed in the alignment of molecules for the indirect approach, respectively.

Mapping and fitting the peripheral benzodiazepine receptor binding site by carboxamide derivatives. Comparison of different approaches to quantitative ligand-receptor interaction modeling

The synthetic-computational approach to the study of the binding site of peripheral benzodiazepine receptor (PBR) ligands related to 1-(2-chlorophenyl)-N-methyl-N-(1-methylpropyl)-3-isoquinolinecarboxamide (PK11195, 1) within their receptor (Cappelli et al. J. Med. Chem. 1997, 40, 2910-2921) has been extended. A series of carboxamide derivatives endowed with differently substituted planar aromatic or heteroaromatic systems was designed with the aim of getting further information on the topological requisites of the carbonyl and aromatic moieties for interaction with the PBR binding site. The synthesis of most of these compounds involves Weinreb amidation of the appropriate lactone as the key step. The most potent compound, among the newly synthesized ones, shows a nanomolar PBR affinity similar to that shown by 1 and the presence of a basic N-ethyl-N-benzylaminomethyl group in 3-position of the quinoline nucleus. Thus, it may be considered the first example of a new class of water soluble derivatives of 1. Several computational methods were used to furnish descriptors of the isolated ligands (indirect approaches) able to rationalize the variation in the binding affinity of the enlarged series of compounds. Sound QSAR models are obtained by size and shape descriptors (volume approach) which codify for the short-range contributions to ligand-receptor interactions. Molecular descriptors which explicitly account for the electrostatic contribution to the interaction (CoMFA, CoMSIA, and surface approaches) perform well, but they do not improve the quantitative models. Moreover, useful hints for the identification of the antagonist binding site in the three-dimensional modeling of the receptor (direct approach) were provided by the receptor hypothesis derived by the pharmacophoric approach. The ligand-receptor complexes obtained provided a detailed description of the modalities of the interaction and interesting suggestions for further experiments.

Retrospective Analysis of an Experimental High-Throughput Screening Data Set by Recursive Partitioning

With the emergence of combinatorial chemistry, whether based on parallel, mixture, solution, or solid phase chemistry, it is now possible to generate large numbers of diverse or focused compound libraries. In this paper we aim to demonstrate that it is possible to design targeted libraries by applying nonparametric statistical methods, recursive partitioning in particular, to large data sets containing thousands of compounds and their associated biological data. Moreover, when applied to an experimental high-throughput screening (HTS) data set, our data strongly suggest that this method can improve the hit rate of our primary screens (about 4- to 5-fold) while increasing screening efficiency: less than one-fifth of the complete selection needs to be screened in order to identify about 75% of all actives present.