New PPARα/γ/δ Optimal Activator Rationally Designed by Computational Methods

Abstract

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 PPARγ 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.Keywords: type 2 diabetes, PPAR pan agonist, molecular modeling, ADMET prediction IntroductionThe general cause of mortality among type 2 diabetes mellitus patients is due to dyslipidemia leading to cardiovascular complications. Currently, the drugs used to control these disorders act separately either on reducing blood glucose or lowering triglyceride levels, free fatty acid and low-density lipoprotein. However, the increasing number of cases of patients with diabetic metabolic syndrome requires the development of therapies that act simultaneously reducing glicidic and lipidic levels in a combined effort to ease cardiovascular disorders. 1 Peroxisome proliferatoractivated receptors (PPARs) are nuclear receptors that act as transcription factors, regulating glucose homeostasis, lipid metabolism and inflammation signaling, making them attractive targets for the development of new therapies for metabolic syndromes. They regulate the expression of target genes after forming a heterodimer with 9-cis-retinoic acid receptor (RXR) and bind to the peroxisome proliferators response elements (PPRE) in the regulatory region of the target gene. The increased transcriptional rates of their target genes may be increased after interaction with an agonist ligand which alters the conformation of PPAR, exposing the DNA (deoxyribonucleic acid) binding site. Three distinct receptors have already been described, PPARα, PPARδ and PPARγ. 2 Padilha et al. 1637 Vol. 27, No. 9, 2016 PPARγ plays an important role in the regulation of glucose and lipid metabolism and it is widely distributed in adipose tissue. 3,4 In adipocytes, the PPARγ activity regulates the expression of genes involved in lipid metabolism, 5-7 in addition to the control of the expression of proteins involved in the uptake of lipids by adipocytes. 8 The PPARγ activation in adipose tissue presents an indirect activity in tissues which respond to insulin. 9 This e...

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

“…Pharmacophore perception for the compounds was carried out using PharmaGist, and the top-ranked pre-alignment of the compounds had a score of 64.605 (see Figure 2), wherein Silva [22] and Padilha [23] independently reported similar score values in their studies, using the same the PharmaGist algorithm. The aligned compounds shared six spatial characteristics: two aromatic (AR) groups and four hydrogen acceptor (HA) groups, where the coordinates and the radius of the spatial characteristics are shown in Table 1.…”

“…The other models were unsatisfactory in the recovery of the known bioactive ligands, since many hypotheses did not obtain the necessary characteristics for interaction at the A2AAR receptor site [26]. Pharmacophore hypothese was classified according to the number of ligands aligned or that combined in order to generate a pharmacophore model that was satisfactory based on score, according to literature data [22,23,26]. The pharmacophore model chosen in pre-alignment via PharmaGist was used as input in the Discovery Studio, in order to refine the initial pharmacophore model.…”

“…The QSAR model was built with n samples of 16 structures (1, 6, 7, 9-21), since 5 structures were outliers (2-5 and 8-randomic errors contaminated the observations) and they were identified and subsequently removed in order to obtain models with greater predictive power, without impairing the statistical quality that was evaluated by the correlation coefficient (r), squared correlation coefficient (r 2 ), explained variance (r 2 A , i.e., r 2 adjusted), standard error of estimate (SEE), and variance ratio (F). The best regression models were selected based on the higher values of r and F and smaller values of SEE [23]. This was proven through the application of the models to the compounds for verifying the variation between the experimental and theoretical values, calculated through the quantitative structure-activity relationship (QSAR) model.…”

“…After evaluating the parameters and the statistical quality, the parametric models (tetra-, pentaand hexaparametric) were applied to the set of seven compounds (22)(23)(24)(25)(26)(27)(28) in this study, called the test set (totaling 43.75% in relation to the number of compounds used in the training set; n = 16). Compounds were selected from the Pubchem database based on their respective EC 50 values, which were converted to pEC 50 .…”

To Probe Full and Partial Activation of Human Peroxisome Proliferator-Activated Receptors by Pan-Agonist Chiglitazar Using Molecular Dynamics Simulations

“…By the use of a computational approach, research has studied protein-ligand interactions at the PPAR receptor that have successfully provided experimentally verified detailed structural information at the molecular level and identified a large number PPAR agonists [8,22,36,38,63,[68][69][70][71][72][73][74][75][76][77][78][79][80]. These include a number studies combining virtual screening with molecular modeling [64,69], molecular docking [36], molecular dynamics (MD) simulations [81], and in vitro assays [38,68] for further validation.…”