Co-crystal structure guided array synthesis of PPARγ inverse agonists

The PPAR Ω Pocket: Renewed Opportunities for Drug Development

The past decade of PPARγ research has dramatically improved our understanding of the structural and mechanistic bases for the diverging physiological effects of different classes of PPARγ ligands. The discoveries that lie at the heart of these developments have enabled the design of a new class of PPARγ ligands, capable of isolating central therapeutic effects of PPARγ modulation, while displaying markedly lower toxicities than previous generations of PPARγ ligands. This review examines the emerging framework around the design of these ligands and seeks to unite its principles with the development of new classes of ligands for PPARα and PPARβ/δ. The focus is on the relationships between the binding modes of ligands, their influence on PPAR posttranslational modifications, and gene expression patterns. Specifically, we encourage the design and study of ligands that primarily bind to the Ω pockets of PPARα and PPARβ/δ. In support of this development, we highlight already reported ligands that if studied in the context of this new framework may further our understanding of the gene programs regulated by PPARα and PPARβ/δ. Moreover, recently developed pharmacological tools that can be utilized in the search for ligands with new binding modes are also presented.

Definition of functionally and structurally distinct repressive states in the nuclear receptor PPARγ

The repressive states of nuclear receptors (i.e., apo or bound to antagonists or inverse agonists) are poorly defined, despite the fact that nuclear receptors are a major drug target. Most ligand bound structures of nuclear receptors, including peroxisome proliferator-activated receptor γ (PPARγ), are similar to the apo structure. Here we use NMR, accelerated molecular dynamics and hydrogen-deuterium exchange mass spectrometry to define the PPARγ structural ensemble. We find that the helix 3 charge clamp positioning varies widely in apo and is stabilized by efficacious ligand binding. We also reveal a previously undescribed mechanism for inverse agonism involving an omega loop to helix switch which induces disruption of a tripartite salt-bridge network. We demonstrate that ligand binding can induce multiple structurally distinct repressive states. One state recruits peptides from two different corepressors, while another recruits just one, providing structural evidence of ligand bias in a nuclear receptor.

The repressive states of peroxisome proliferator-activated receptor γ (PPARγ) are ill-defined, despite nuclear receptors being a major drug target. Here authors demonstrate multiple structurally distinct repressive states, providing a structural rationale for ligand bias in a nuclear receptor.

A structural mechanism for directing corepressor-selective inverse agonism of PPARγ

Small chemical modifications can have significant effects on ligand efficacy and receptor activity, but the underlying structural mechanisms can be difficult to predict from static crystal structures alone. Here we show how a simple phenyl-to-pyridyl substitution between two common covalent orthosteric ligands targeting peroxisome proliferator-activated receptor (PPAR) gamma converts a transcriptionally neutral antagonist (GW9662) into a repressive inverse agonist (T0070907) relative to basal cellular activity. X-ray crystallography, molecular dynamics simulations, and mutagenesis coupled to activity assays reveal a water-mediated hydrogen bond network linking the T0070907 pyridyl group to Arg288 that is essential for corepressor-selective inverse agonism. NMR spectroscopy reveals that PPARγ exchanges between two long-lived conformations when bound to T0070907 but not GW9662, including a conformation that prepopulates a corepressor-bound state, priming PPARγ for high affinity corepressor binding. Our findings demonstrate that ligand engagement of Arg288 may provide routes for developing corepressor-selective repressive PPARγ ligands.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a target for insulin sensitizing drugs. Here the authors combine NMR, X-ray crystallography and MD simulations and report a structural mechanism for eliciting PPARγ inverse agonism, where coactivator binding is inhibited and corepressor binding promoted, which causes PPARγ repression.

Synthesis, in vitro evaluation, and molecular modeling investigation of benzenesulfonimide peroxisome proliferator-activated receptors α antagonists

Recent evidences suggest a moderate activation of Peroxisome Proliferator-Activated Receptors (PPARs) could be favorable in metabolic diseases, reducing side effects given from full agonists. PPAR partial agonists and antagonists represent, to date, interesting tools to better elucidate biological processes modulated by these receptors. In this work are reported new benzenesulfonimide compounds able to block PPARα, synthesized and tested by transactivation assays and gene expression analysis. Some of these compounds showed a dose-dependent antagonistic behavior on PPARα, submicromolar potency, different profiles of selectivity versus PPARγ, and a repressive effect on CPT1A expression. Dockings and molecular dynamics on properly selected benzenesulfonimide derivatives furnished fresh insights into the molecular determinant most likely responsible for PPARα antagonism.

Review of the Structural and Dynamic Mechanisms of PPARγ Partial Agonism

PPARγ (peroxisome proliferator activated receptor γ) is a ligand activated transcription factor of the nuclear receptor superfamily that controls the expression of a variety of genes involved in fatty acid metabolism, adipogenesis, and insulin sensitivity. While endogenous ligands of PPARγ include fatty acids and eicosanoids, synthetic full agonists of the receptor, including members of the thiazolidinedione (TZD) class, have been widely prescribed for the treatment of type II diabetes mellitus (T2DM). Unfortunately, the use of full agonists has been hampered by harsh side effects with some removed from the market in many countries. In contrast, partial agonists of PPARγ have been shown to retain favourable insulin sensitizing effects while exhibiting little to no side effects and thus represent a new potential class of therapeutics for the treatment of T2DM. Partial agonists have been found to not only display differences in transcriptional and cellular outcomes, but also act through distinct structural and dynamic mechanisms within the ligand binding cavity compared to full agonists.

Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands

Peroxisome proliferator-activated receptor-gamma (PPARγ) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40 %) when compared to docking with a single structure model (<20 %). Prediction was also improved when specific protein-ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPARγ ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein-ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPARγ that could serve as candidates for therapeutic development.

SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates.

Virtual Screening as a Technique for PPAR Modulator Discovery

Virtual screening (VS) is a discovery technique to identify novel compounds with therapeutic and preventive efficacy against disease. Our current focus is on the in silico screening and discovery of novel peroxisome proliferator-activated receptor-gamma (PPARgamma) agonists. It is well recognized that PPARgamma agonists have therapeutic applications as insulin sensitizers in type 2 diabetes or as anti-inflammatories. VS is a cost- and time-effective means for identifying small molecules that have therapeutic potential. Our long-term goal is to devise computational approaches for testing the PPARgamma-binding activity of extensive naturally occurring compound libraries prior to testing agonist activity using ligand-binding and reporter assays. This review summarizes the high potential for obtaining further fundamental understanding of PPARgamma biology and development of novel therapies for treating chronic inflammatory diseases through evolution and implementation of computational screening processes for immunotherapeutics in conjunction with experimental methods for calibration and validation of results.

Structure-guided design of N-phenyl tertiary amines as transrepression-selective liver X receptor modulators with anti-inflammatory activity

A cocrystal structure of T1317 (3) bound to hLXRbeta was utilized in the design of a series of substituted N-phenyl tertiary amines. Profiling in binding and functional assays led to the identification of LXR modulator GSK9772 ( 20) as a high-affinity LXRbeta ligand (IC 50 = 30 nM) that shows separation of anti-inflammatory and lipogenic activities in human macrophage and liver cell lines, respectively. A cocrystal structure of the structurally related analog 19 bound to LXRbeta reveals regions within the receptor that can affect receptor modulation through ligand modification. Mechanistic studies demonstrate that 20 is greater than 10-fold selective for LXR-mediated transrepression of proinflammatory gene expression versus transactivation of lipogenic signaling pathways, thus providing an opportunity for the identification of LXR modulators with improved therapeutic indexes.