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Lead Identification for Modulators of Multidrug Resistance based on in silico Screening with a Pharmacophoric Feature Model
Author(s) -
Langer Thierry,
Eder Monika,
Hoffmann Remy D.,
Chiba Peter,
Ecker Gerhard F.
Publication year - 2004
Publication title -
archiv der pharmazie
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.468
H-Index - 61
eISSN - 1521-4184
pISSN - 0365-6233
DOI - 10.1002/ardp.200300817
Subject(s) - pharmacophore , quantitative structure–activity relationship , chemistry , in silico , virtual screening , propafenone , combinatorial chemistry , molecular model , stereochemistry , computational biology , biochemistry , biology , medicine , cardiology , gene , atrial fibrillation
Considerable effort has been devoted to the characterization of P‐glycoprotein — drug interaction in the past. Systematic quantitative structure‐activity relationship (QSAR) studies identified both predictive physicochemical parameters and pharmacophoric substructures within homologous series of compounds. Comparative molecular field analysis (CoMFA) led to distinct 3D‐QSAR models for propafenone and phenothiazine analogs. Recently, several pharmacophore models have been generated for diverse sets of ligands. Starting from a training set of 15 propafenone‐type MDR‐modulators, we established a chemical function‐based pharmacophore model. The pharmacophoric features identified by this model were (i) one hydrogen bond acceptor, (ii) one hydrophobic area, (iii) two aromatic hydrophobic areas, and (iv) one positive ionizable group. In silico screening of the Derwent World Drug Index using the model led to identification of 28 compounds. Substances retrieved by database screening are diverse in structure and include dihydropyridines, chloroquine analogs, phenothiazines, and terfenadine. On the basis of its general applicability, the presented 3DQSAR model allows in silico screening of virtual compound libraries to identify new potential lead compounds.