Comparison of the binding pockets of two chemically unrelated allosteric antagonists of the mGlu5 receptor and identification of crucial residues involved in the inverse agonism of MPEP.

Fenobam [N-(3-chlorophenyl)-N'-(4,5-dihydro-1-methyl-4-oxo-1H-imidazole-2-yl)urea], a clinically validated non-benzodiazepine anxiolytic, has been shown to be a potent and non-competitive metabotropic glutamate (mGlu)-5 receptor antagonist. In the present study, we have used the site-directed mutagenesis coupled with three-dimensional receptor-based pharmacophore modelling to elucidate the interacting mode of fenobam within the seven-transmembrane domain (7TMD) of mGlu5 receptor and its comparison with that of 2-methyl-6-(phenylethynyl)pyridine (MPEP), the prototype antagonist. The common residues involved in the recognition of MPEP and fenobam include Pro654(3.36), Tyr658(3.40), Thr780(6.44), Trp784(6.48), Phe787(6.51), Tyr791(6.55) and Ala809(7.47). The differentiating residues between both modulators' interacting modes are Arg647(3.29), Ser657(3.39) and Leu743(5.47). Our data suggest that these chemically unrelated mGlu5 antagonists act similarly, probing a functionally unique region of the 7TMD. Using [3H]inositol phosphates accumulation assay, we have also identified the critical residues involved in the inverse agonist effect of MPEP. The mutation W784(6.48)A completely blocked the inverse agonist activity of MPEP; two mutations F787(6.51)A and Y791(6.55)A, caused a drastic decrease in the MPEP inverse agonism. Furthermore, these three mutations led to an increased efficacy of quisqualate without having any effect on its potency. The fact that the residues Trp784(6.48) and Phe787(6.51) are essential equally in antagonism and inverse agonism effects emphasizes again the key role of these residues and the involvement of a common transmembrane network in receptor inactivation by MPEP.

An automated system for the analysis of G protein-coupled receptor transmembrane binding pockets: alignment, receptor-based pharmacophores, and their application.

G protein-coupled receptors (GPCRs) share a common architecture consisting of seven transmembrane (TM) domains. Various lines of evidence suggest that this fold provides a generic binding pocket within the TM region for hosting agonists, antagonists, and allosteric modulators. Here, a comprehensive and automated method allowing fast analysis and comparison of these putative binding pockets across the entire GPCR family is presented. The method relies on a robust alignment algorithm based on conservation indices, focusing on pharmacophore-like relationships between amino acids. Analysis of conservation patterns across the GPCR family and alignment to the rhodopsin X-ray structure allows the extraction of the amino acids lining the TM binding pocket in a so-called ligand binding pocket vector (LPV). In a second step, LPVs are translated to simple 3D receptor pharmacophore models, where each amino acid is represented by a single spherical pharmacophore feature and all atomic detail is omitted. Applications of the method include the assessment of selectivity issues, support of mutagenesis studies, and the derivation of rules for focused screening to identify chemical starting points in early drug discovery projects. Because of the coarseness of this 3D receptor pharmacophore model, however, meaningful scoring and ranking procedures of large sets of molecules are not justified. The LPV analysis of the trace amine-associated receptor family and its experimental validation is discussed as an example. The value of the 3D receptor model is demonstrated for a class C GPCR family, the metabotropic glutamate receptors.

Predicting plasma protein binding of drugs--revisited.

Plasma protein binding of drugs has been studied for almost 100 years, but despite the accumulation of large amounts of data, the accurate prediction of this ADME parameter continues to be problematic. This review outlines recent efforts on the development of prediction tools for plasma protein binding of drugs, specifically human serum albumin, in the context of its relevance and its influencing factors. The issue of why it is difficult to achieve prediction of sufficient quality for a diverse dataset will also be considered.

Predicting plasma protein binding of drugs: a new approach.

In spite of the large amount of plasma protein binding data for drugs, it is not obvious and there is no clear consensus among different disciplines how to deal with this parameter in multidimensional lead optimization strategies. In this work, we have made a comprehensive study on the importance of plasma protein binding and the influencing factors in order to get new insights for this molecular property. Our analysis of the distribution of percentage plasma protein binding among therapeutic drugs showed that no general rules for protein binding can be derived, except for the class of chemotherapeutics, where a clear trend towards lower binding could be observed. For the majority of indication areas, however, empirical rules are missing. We present here an extensive list of multiply determined primary association constants for binding to human serum albumin (HSA) for 138 compounds from the literature. Correlating these binding constants with the percentage fraction of protein bound showed that the percentage data above 90%, corresponding to a binding constant below 6 microM, are of insufficient accuracy. Furthermore, it could be demonstrated that the lipophilicity of drugs, traditionally felt to dominate binding to HSA, is not the only relevant descriptor. Here, we report a generic model for the prediction of drug association constants to HSA, which uses a pharmacophoric similarity concept and partial least square analysis (PLS) to construct a quantitative structure-activity relationship. It is able to single out the submicromolar to nanomolar binders, i.e. to differentiate between 99.0 and 99.99% plasma protein binding. Depending on the system, this can be important in medicinal chemistry programs and may together with other computed physicochemical and ADME properties assist in the prioritization of synthetic strategies.