TY - JOUR
T1 - New PPARα/γ/δ optimal activator rationally designed by computational methods
AU - Padilha, Elias C.
AU - Serafim, Rodolfo B.
AU - Sarmiento, Deisy Y.R.
AU - Santos, César F.
AU - Santos, Cleydson B.R.
AU - Silva, Carlos H.T.P.
N1 - Publisher Copyright:
©2016 Sociedade Brasileira de Química.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - The peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor that acts as a transcription factor, regulating glucose, lipid and inflammation signaling and it is exploited in type 2 diabetes treatment. However, the selective activation of this PPAR subtype has been linked to important adverse effects which can be mitigated through concomitant activation of PPARα and PPARδ. In this study, we proposed new PPARγ agonists using PharmaGist Server for pharmacophore prediction, the molecular docking was performed by GOLD (genetic optimization for ligand docking) v2.2, AutoDock 4.2 and AutoDock Vina 1.1 and QikProp v4.0 and Derek for absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment. One molecule showed high predicted affinity to PPARδ and favorable pharmacokinetic and toxicity properties. It was then evaluated against PPARα and PPARδ and showed greater affinity to these receptors than the controls. Therefore this molecule is a promising drug lead for the development of derivatives and for the treatment of metabolic syndrome with the benefits of a PPAR pan activation.
AB - The peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor that acts as a transcription factor, regulating glucose, lipid and inflammation signaling and it is exploited in type 2 diabetes treatment. However, the selective activation of this PPAR subtype has been linked to important adverse effects which can be mitigated through concomitant activation of PPARα and PPARδ. In this study, we proposed new PPARγ agonists using PharmaGist Server for pharmacophore prediction, the molecular docking was performed by GOLD (genetic optimization for ligand docking) v2.2, AutoDock 4.2 and AutoDock Vina 1.1 and QikProp v4.0 and Derek for absorption, distribution, metabolism, excretion and toxicity (ADMET) assessment. One molecule showed high predicted affinity to PPARδ and favorable pharmacokinetic and toxicity properties. It was then evaluated against PPARα and PPARδ and showed greater affinity to these receptors than the controls. Therefore this molecule is a promising drug lead for the development of derivatives and for the treatment of metabolic syndrome with the benefits of a PPAR pan activation.
KW - ADMET prediction
KW - Molecular modeling
KW - PPAR pan agonist
KW - Type 2 diabetes
UR - http://www.scopus.com/inward/record.url?scp=84988360151&partnerID=8YFLogxK
U2 - 10.5935/0103-5053.20160043
DO - 10.5935/0103-5053.20160043
M3 - Artículo Científico
AN - SCOPUS:84988360151
SN - 0103-5053
VL - 27
SP - 1636
EP - 1647
JO - Journal of the Brazilian Chemical Society
JF - Journal of the Brazilian Chemical Society
IS - 9
ER -
