A Novel Multilevel Statistical Method for the Study of the Relationships between Multireceptorial Binding Affinity Profiles and In Vivo Endpoints

Abstract

The present work introduces a novel method for drug research based on the sequential building of linked multivariate statistical models, each one introducing a different level of drug description. The use of multivariate methods allows us to overcome the traditional one-target assumption and to link in vivo endpoints with drug binding profiles, involving multiple receptors. The method starts with a set of drugs, for which in vivo or clinical observations and binding affinities for potentially relevant receptors are known, and allows obtaining predictions of the in vivo endpoints highlighting the most influential receptors. Moreover, provided that the structure of the receptor binding sites is known (experimentally or by homology modeling), the proposed method also highlights receptor regions and ligandreceptor interactions that are more likely to be linked to the in vivo endpoints, which is information of high interest for the design of novel compounds. The method is illustrated by a practical application dealing with the study of the metabolic side effects of antipsychotic drugs. Herein, the method detects related receptors confirmed by experimental results. Moreover, the use of structural models of the receptor binding sites allows identifying regions and ligand-receptor interactions that are involved in the discrimination between antipsychotic drugs that show metabolic side effects and those that do not. The structural results suggest that the topology of a hydrophobic sandwich involving residues in transmembrane helices (TM) 3, 5, and 6, as well as the assembling of polar residues in TM5, are important discriminators between target/antitarget receptors. Ultimately, this will provide useful information for the design of safer compounds inducing fewer side effects.The classic formulation in which a disease is associated to a single biological receptor is clearly an oversimplification, valid only for exceptional situations. Recent advances in genomics, proteomics, and systems biology depict a far more complex scenario, much less comfortable to work with, in which the biochemical mechanisms of diseases and therapies involve many different biological receptors linked in complex interaction pathways, which are usually poorly understood. The best possible therapies for many complex diseases will probably require a deep and detailed understanding of those relationships. However, because of the extreme complexity of the involved mechanisms, we will not achieve such detailed understanding in the immediate future. Hence, there is a need for new methodological approaches that are able to advance in this way. In this work, we propose a novel method for exploiting pharmacologic information obtained for sets of currently available drugs that are known to interact with multiple related receptors. This method is intended to improve our understanding of the biological mechanisms implicated in the pharmacological treatments but also to provide hints about how the chemical structure of the studied drugs could be improve...

Progress in the structural prediction of G protein‐coupled receptors: D3 receptor in complex with eticlopride

“…Particularly, residues F6.51 and F6.52 establish π‐stacking interactions (T‐shaped type) with the aromatic ring of eticlopride and form a hydrophobic sandwich, which consists of F6.51 and F6.52 with V3.33. This key interaction stabilizes the aromatic ring of the ligand and coincides with the carazolol‐β 2 adrenergic receptor complex (PDB ID: 2RH1) as well as with our previous work for tricyclic ligands in different GPCRs subtypes 23…”

“…This key interaction stabilizes the aromatic ring of the ligand and coincides with the carazolol-b 2 adrenergic receptor complex (PDB ID: 2RH1) as well as with our previous work for tricyclic ligands in different GPCRs subtypes. 23 A comparison of the profile of the ligand-receptor interaction for all poses reveals that pose 1c, which already proved to have an identical ligand conformation to the X-ray structure, also resembles the native crystal structure [ Fig. 3(B)].…”

Current Concepts and Treatments of Schizophrenia

“…The GPCR signaling mechanisms described above have been considered as potential drug targets for novel antipsychotics. The most commonly exploited approach is intentional ligand promiscuity for multi-target drugs typical for second- and, to a lesser extent, first-generation antipsychotics which requires separation of the drug targets from the off-targets [ 157 ]. The treatment of complex diseases, such as Alzheimer’s disease, Parkinson’s disease, cancer or schizophrenia, which involve multiple receptors and enzymes in their pathomechanisms, require looking for potential drugs which satisfy the criteria of many pharmacophores, oppositely to acting on a single molecular target.…”

Structure-Based Discovery of Selective Serotonin 5-HT 1B Receptor Ligands

“…Discovery of Selective 5-HT 1B Receptor Ligands elegant example is the chemoinformatic design of selective D 4 dopamine receptor ligands by Hopkins and colleagues (Besnard et al, 2012). Homology models have also been used to analyze drug selectivity profiles for aminergic GPCRs (Chien et al, 2010;Selent et al, 2010;Selent et al, 2008). However, prospective screening for selective GPCR ligands based on homology models has had limited success (Kolb et al, 2012).…”