GPCR antitarget modeling: pharmacophore models for biogenic amine binding GPCRs to avoid GPCR-mediated side effects

The α 1-adrenolytic and structural evaluation of new pyridoindole derivatives

α1 — adrenolytic activities of pyridoindole derivatives recently synthesized in the Institute of Experimental Pharmacology, Slovak Academy of Sciences, were measured. A characteristic set of derivatives (five active, one with a threshold activity and two inactive) was chosen and an elementary structure-activity study was performed. The structure and energy properties were estimated by quantum-chemical semiempirical AM1 and molecular mechanics methods. The ionization constants pKα of the individual derivatives were calculated by program Pallas or estimated by potenciometric titration. The α 1 blocking activities were measured by rat thoracic aorta model. The experimental model used was not α 1 — adrenoreceptor subtypes specific, however, the α 1D subtype could be considered to be a predominant type in a rat aorta. The obtained physico-chemical parameters were then compared with the blocking activities of the derivatives and following determining characters for α 1 — adrenolytic activities were determined: 1) the polarity of the substituted phenol ring represented by a map of molecular electrostatic potential and 2) the hexahydro-pyridine nitrogen pKα constant, which represents the ability of the compound to be protonated by physiologic pH.

The 7 TM G-protein-coupled receptor target family

Chemical biology approaches have a long history in the exploration of the G-protein-coupled receptor (GPCR) family, which represents the largest and most important group of targets for therapeutics. The analysis of the human genome revealed a significant number of new members with unknown physiological function which are today the focus of many reverse pharmacology drug-discovery programs. As the seven hydrophobic transmembrane segments are a defining common structural feature of these receptors, and as signaling through heterotrimeric G proteins is not demonstrated in all cases, these proteins are also referred to as seven transmembrane (7 TM) or serpentine receptors. This review summarizes important historic milestones of GPCR research, from the beginning, when pharmacology was mainly descriptive, to the age of modern molecular biology, with the cloning of the first receptor and now the availability of the entire human GPCR repertoire at the sequence and protein level. It shows how GPCR-directed drug discovery was initially based on the careful testing of a few specifically made chemical compounds and is today pursued with modern drug-discovery approaches, including combinatorial library design, structural biology, molecular informatics, and advanced screening technologies for the identification of new compounds that activate or inhibit GPCRs specifically. Such compounds, in conjunction with other new technologies, allow us to study the role of receptors in physiology and medicine, and will hopefully result in novel therapies. We also outline how basic research on the signaling and regulatory mechanisms of GPCRs is advancing, leading to the discovery of new GPCR-interacting proteins and thus opening new perspectives for drug development. Practical examples from GPCR expression studies, HTS (high-throughput screening), and the design of monoamine-related GPCR-focused combinatorial libraries illustrate ongoing GPCR chemical biology research. Finally, we outline future progress that may relate today's discoveries to the development of new medicines.

Chemogenomics: structuring the drug discovery process to gene families

In the post-genomic era, if all proteins in a gene family can putatively be identified, how can drug discovery effectively tackle so many novel targets that might lack structural and small-molecule inhibitory data? In response, chemogenomics, a new approach that guides drug discovery based on gene families, has been developed. By integrating all information available within a protein family (sequence, SAR data, protein structure), chemogenomics can efficiently enable cross-SAR exploitation, directed compound selection and early identification of optimum selectivity panel members. This review examines recent developments in chemogenomics technologies and illustrates their predictive capabilities with successful examples from two of the major protein families: protein kinases and G-protein-coupled receptors.

High-throughput structure-based pharmacophore modelling as a basis for successful parallel virtual screening

In order to assess bioactivity profiles for small organic molecules we propose to use parallel pharmacophore-based virtual screening. Our aim is to provide a fast, reliable and scalable system that allows for rapid in silico activity profile prediction of virtual molecules. In this proof of principle study, carried out with the new structure-based pharmacophore modelling tool LigandScout and the high-performance database mining platform Catalyst, we present a model work for the application of parallel pharmacophore-based virtual screening on a set of 50 structure-based pharmacophore models built for various viral targets and 100 antiviral compounds. The latter were screened against all pharmacophore models in order to determine if their known biological targets could be correctly predicted via an enrichment of corresponding pharmaco-phores matching these ligands. The results demonstrate that the desired enrichment, i.e. a successful activity profiling, was achieved for approximately 90% of all input molecules. Additionally, we discuss descriptors for output validation, as well as various aspects influencing the analysis of the obtained activity profiles, and the effect of the searching mode utilized for screening. The results of the study presented here clearly indicate that pharmacophore-based parallel screening comprises a reliable in silico method to predict the potential biological activities of a compound or a compound library by screening it against a series of pharmacophore queries.

Selective pharmacophore design for α1-adrenoceptor subtypes

Alpha1-adrenoceptors are G-protein coupled receptors found in a variety of vascular tissues and responsible for vasoconstriction. Selectivity for each of the three subtypes is an important consideration in drug design in order to minimise the possibility of side effects. Using Catalyst we developed ligand-based pharmacophores from alpha(1a,b,d)-selective antagonists available in the literature using three separate training sets. Four-feature pharmacophores were developed for the alpha(1a) and alpha(1b) subtype-selective antagonists and a five-feature pharmacophore was developed for the alpha(1d) subtype-selective antagonists. The alpha(1a) pharmacophore represents both class I and II compounds with good predictivity for other compounds outside the training set as well. The alpha(1b) pharmacophore best predicts the activity of prazosin analogues as these make up the majority of alpha(1b)-selective antagonists. Unexpectedly, no positive ionisable feature was incorporated in the alpha(1b) pharmacophore. The alpha(1d) pharmacophore was based primarily on one structural class of compounds, but has good predictivity for a heterogeneous test set. Preliminary docking studies using AutoDock and optimised alpha1-adrenoceptor homology models, conducted with the antagonists prazosin (32) and 66, showed good agreement with the findings from the pharmacophores.