Format

Send to

Choose Destination
Bioorg Med Chem. 2012 Jun 1;20(11):3523-32. doi: 10.1016/j.bmc.2012.04.005. Epub 2012 Apr 19.

Activity landscape modeling of PPAR ligands with dual-activity difference maps.

Author information

1
Facultad de Química, Departamento de Farmacia, Universidad Nacional Autónoma de México, México DF 04510, Mexico.

Abstract

Activation of peroxisome proliferator-activated receptor (PPAR) subtypes offers a promising strategy for the treatment of diabetes mellitus and metabolic diseases. Selective and dual PPAR agonists have been developed and the systematic characterization of their structure-activity relationships (SAR) is of major significance. Herein, we report a systematic description of the SAR of 168 compounds screened against the three PPAR subtypes using the principles of activity landscape modeling. As part of our effort to develop and apply chemoinformatic tools to navigate through activity landscapes, we employed consensus dual-activity difference maps recently reported. The analysis is based on pairwise relationships of potency difference and structure-similarity which were calculated from the combination of four different 2D and 3D structure representations. Dual-activity difference maps uncovered regions in the landscape with similar SAR for two or three receptor subtypes as well as regions with inverse SAR, that is, changes in structure that increase activity for one subtype but decrease activity for the other subtype. Analysis of pairs of compounds with high structure similarity revealed the presence of single-, dual-, and 'pan-receptor' activity cliffs, that is, small changes in structure with high changes in potency for one, two, or three receptor subtypes, respectively. Single-, dual-, and pan-receptor scaffold hops are also discussed. The analysis of the chemical structures of selected data points reported in this paper points to specific structural features that are helpful for the design of new PPAR agonists. The approach presented in this work is general and can be extended to analyze larger data sets.

PMID:
22564380
DOI:
10.1016/j.bmc.2012.04.005
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center