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 ligand-receptor 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.
Footnotes
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The online version of this article (available at http://molpharm.aspetjournals.org) contains supplemental material.
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This work was supported by the Spanish Ministry of Science and Innovation [Grant SAF2005-08025-C03], the Instituto de Salud Carlos III for projects RETICS HERACLES [Grant RD06/0009] and COMBIOMED [Grant RD07/0067], and the Departament d'Innovació, Universitat i Empresa de la Generalitat de Catalunya. The Research Unit on Biomedical Informatics (GRIB, InstitutMunicipal d'Investigació Mèdica de l'Hospital del Mar y la Universitat Pompeu Fabra) is the Biomedical Informatics Node of the Spanish Institute of Bioinformatics (INB).
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Article, publication date, and citation information can be found at http://molpharm.aspetjournals.org.
doi:10.1124/mol.109.060103
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ABBREVIATIONS:
- PCA
- principal component analysis
- PLS-R
- partial least square regression
- APD
- antipsychotic drug
- 5-HT
- 5-hydroxytryptamine
- GPCR
- G-protein coupled receptor
- LV
- latent variable
- WG
- weight gain
- TM
- transmembrane helix.
- Received August 6, 2009.
- Accepted November 9, 2009.
- Copyright © 2010 The American Society for Pharmacology and Experimental Therapeutics
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