Structure-Based Drug Design of a Novel Family of PPARγ Partial Agonists: Virtual Screening, X-ray Crystallography, and in Vitro/in Vivo Biological Activities

Understanding nuclear receptors using computational methods

Nuclear receptors (NRs) are important targets for therapeutic drugs. NRs regulate transcriptional activities through binding to ligands and interacting with several regulating proteins. Computational methods can provide insights into essential ligand-receptor and protein-protein interactions. These in turn have facilitated the discovery of novel agonists and antagonists with high affinity and specificity as well as have aided in the prediction of toxic side effects of drugs by identifying possible off-target interactions. Here, we review the application of computational methods toward several clinically important NRs (with special emphasis on PXR) and discuss their use for screening and predicting the toxic side effects of xenobiotics.

Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening

HipHop program was used to generate a common chemical feature hypothesis for pan Peroxisome Proliferator-Activated Receptor (PPAR) agonists. The top scoring hypothesis (hypo-1) was found to differentiate the pan agonists (actives) from subtype-specific and dual PPAR agonists (inactives). The importance of individual features in hypo-1 was assessed by deleting a particular feature to generate a new hypothesis and observing its discriminating ability between 'actives' and 'inactives'. Deletion of aromatic features AR-1 (hypo-1b), AR-2 (hypo-1e) and a Hydrophobic feature HYD-1 (hypo-1c) individually did not affect the discriminating power of the hypo-1 significantly. However, deletion of a Hydrogen Bond Acceptor (HBA) feature (hypo-1f) in the hydrophobic tail group was found to be highly detrimental for the specificity of hypo-1 leading to high hit rate of 'inactives'. Since hypo-1 did not produce any useful hits from the database search, hypo-1b, hypo-1c and hypo-1e were used for virtual screening leading to the identification of new potential pan PPAR ligands. The docking studies were used to predict the binding pose of the proposed molecules in PPARgamma active site.

Crystal structure of the peroxisome proliferator-activated receptor gamma (PPARgamma) ligand binding domain complexed with a novel partial agonist: a new region of the hydrophobic pocket could be exploited for drug design

The peroxisome proliferator-activated receptors (PPARs) are ligand-dependent transcription factors regulating glucose and lipid metabolism. The search for new PPAR ligands with reduced adverse effects with respect to the marketed antidiabetic agents thiazolidinediones (TZDs) and the dual-agonists glitazars is highly desired. We report the crystal structure and activity of the two enantiomeric forms of a clofibric acid analogue, respectively complexed with the ligand-binding domain (LBD) of PPARgamma, and provide an explanation on a molecular basis for their different potency and efficacy against PPARgamma. The more potent S-enantiomer is a dual PPARalpha/PPARgamma agonist which presents a partial agonism profile against PPARgamma. Docking of the S-enantiomer in the PPARalpha-LBD has been performed to explain its different subtype pharmacological profile. The hypothesis that partial agonists show differential stabilization of helix 3, when compared to full agonists, is also discussed. Moreover, the structure of the complex with the S-enantiomer reveals a new region of the PPARgamma-LBD never sampled before by other ligands.

Lead Discovery Using Virtual Screening

The practice of virtual screening (VS) to identify chemical leads to known or novel targets is becoming a core function of the computational chemist within industry. By employing a range of techniques, when attempting to identify compounds with activity against a biological target, a small focused subset of a larger collection of compounds can be identified and tested, often with results much better than selecting a similar number of compounds at random. We will review the key methods available, their relative success, and provide practical insights into best practices and key gaps. We will also argue that the capability of VS methods has grown to a point where fuller integration with experimental methods, including HTS, could increase the effectiveness of both.

Bioassay-guided purification and identification of PPARalpha/gamma agonists from Chlorella sorokiniana

This study isolated agonists of peroxisome proliferator activated receptors (PPARs) from the green algae Chlorella sorokiniana, using a bioassay-guided purification strategy. PPARs are widely recognized as the molecular drug targets for many diseases including hyperglycemia, diabetes, obesity and cancer. Two independent bioassays were developed. The first is the scintillation proximity assay, a ligand binding assay. The other is the cell-based transcriptional activation assay which uses the Dual-Luciferase reporter system as the reporter gene under the control of the PPAR response element. Using these two assays, a PPARgamma-active fraction, CE 3-3, was obtained from C. sorokiniana extracts, which was also able to activate PPARalphamediated gene expression. To elucidate the active ingredients in the CE 3-3 fraction, GC-MS analysis was employed. The results showed that the CE 3-3 fraction consisted of at least ten fatty acids (FAs). The bioactivities of several of the individual FAs were evaluated for their PPARgamma activity and the results showed that linolenic acid and linoleic acid were the most potent FAs tested. Our studies indicate that Chlorella sorokiniana could have potential health benefits through the dual activation of PPARalpha/gamma via its unique FA constituents.

Ligand-induced stabilization and activation of peroxisome proliferator-activated receptor gamma

Peroxisome proliferator-activated receptor gamma belongs to the nuclear receptor superfamily and is activated by the antidiabetic drugs rosiglitazone and pioglitazone. Ligand-independent constitutive activity of peroxisome proliferator-activated receptor gamma is also demonstrated. X-ray crystallographic structures show that the active or inactive conformations of the receptor are determined by the position of helix 12 in the C-terminal end. In this study, molecular dynamics simulations were used to gain molecular insight into the activation process and the structural stability of inactive and active peroxisome proliferator-activated receptor gamma receptor structure. The simulations showed: (i) during molecular dynamics simulations without agonist at the active site, the receptor structure with helix 12 in a position corresponding to activated receptor structure was structurally more stable than with helix 12 in a position corresponding to inactive receptor structure, which may contribute to the constitutive activity of the receptor; (ii) docosahexenoic acid stabilized the active receptor conformation more efficiently than the glitazones; (iii) docosahexenoic acid, but not glitazones, induced structural changes into the inactive receptor structure such that helix 12 was shifted into a position more similar to that of an active receptor structure, which indicate that docosahexenoic acid is a more effective peroxisome proliferator-activated receptor gamma agonist than the glitazones.

PPAR-gamma agonists inhibit profibrotic phenotypes in human lung fibroblasts and bleomycin-induced pulmonary fibrosis

Pulmonary fibrosis is characterized by alterations in fibroblast phenotypes resulting in excessive extracellular matrix accumulation and anatomic remodeling. Current therapies for this condition are largely ineffective. Peroxisome proliferator-activated receptor-gamma (PPAR-gamma) is a member of the nuclear hormone receptor superfamily, the activation of which produces a number of biological effects, including alterations in metabolic and inflammatory responses. The role of PPAR-gamma as a potential therapeutic target for fibrotic lung diseases remains undefined. In the present study, we show expression of PPAR-gamma in fibroblasts obtained from normal human lungs and lungs of patients with idiopathic interstitial pneumonias. Treatment of lung fibroblasts and myofibroblasts with PPAR-gamma agonists results in inhibition of proliferative responses and induces cell cycle arrest. In addition, PPAR-gamma agonists, including a constitutively active PPAR-gamma construct (VP16-PPAR-gamma), inhibit the ability of transforming growth factor-beta1 to induce myofibroblast differentiation and collagen secretion. PPAR-gamma agonists also inhibit fibrosis in a murine model, even when administration is delayed until after the initial inflammation has largely resolved. These observations indicate that PPAR-gamma is an important regulator of fibroblast/myofibroblast activation and suggest a role for PPAR-gamma ligands as novel therapeutic agents for fibrotic lung diseases.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening

The use of inaccurate scoring functions in docking algorithms may result in the selection of compounds with high predicted binding affinity that nevertheless are known experimentally not to bind to the target receptor. Such falsely predicted binders have been termed 'binding decoys'. We posed a question as to whether true binders and decoys could be distinguished based only on their structural chemical descriptors using approaches commonly used in ligand based drug design. We have applied the k-Nearest Neighbor (kNN) classification QSAR approach to a dataset of compounds characterized as binders or binding decoys of AmpC beta-lactamase. Models were subjected to rigorous internal and external validation as part of our standard workflow and a special QSAR modeling scheme was employed that took into account the imbalanced ratio of inhibitors to non-binders (1:4) in this dataset. 342 predictive models were obtained with correct classification rate (CCR) for both training and test sets as high as 0.90 or higher. The prediction accuracy was as high as 100% (CCR = 1.00) for the external validation set composed of 10 compounds (5 true binders and 5 decoys) selected randomly from the original dataset. For an additional external set of 50 known non-binders, we have achieved the CCR of 0.87 using very conservative model applicability domain threshold. The validated binary kNN QSAR models were further employed for mining the NCGC AmpC screening dataset (69653 compounds). The consensus prediction of 64 compounds identified as screening hits in the AmpC PubChem assay disagreed with their annotation in PubChem but was in agreement with the results of secondary assays. At the same time, 15 compounds were identified as potential binders contrary to their annotation in PubChem. Five of them were tested experimentally and showed inhibitory activities in millimolar range with the highest binding constant K(i) of 135 microM. Our studies suggest that validated QSAR models could complement structure based docking and scoring approaches in identifying promising hits by virtual screening of molecular libraries.

A Role for the PPARgamma in Cancer Therapy

In 1997, the first published reports highlighted PPARgamma as a novel cancer therapeutic target regulating differentiation of cancer cells. A subsequent flurry of papers described these activities more widely and fuelled further enthusiasm for differentiation therapy, as the ligands for the PPARgamma were seen as well tolerated and in several cases well-established in other therapeutic contexts. This initial enthusiasm and promise was somewhat tempered by contradictory findings in several murine cancer models and equivocal trial findings. As more understanding has emerged in recent years, a renaissance has occurred in targeting PPARgamma within the context of either chemoprevention or chemotherapy. This clarity has arisen in part through a clearer understanding of PPARgamma biology, how the receptor interacts with other proteins and signaling events, and the mechanisms that modulate its transcriptional actions. Equally greater translational understanding of this target has arisen from a clearer understanding of in vivo murine cancer models. Clinical exploitation will most likely require precise and quantifiable description of PPARgamma actions, and resolution of which targets are the most beneficial to target combined with an understanding of the mechanisms that limits its anticancer effectiveness.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

Ajulemic acid, a synthetic nonpsychoactive cannabinoid acid, bound to the ligand binding domain of the human peroxisome proliferator-activated receptor gamma

Ajulemic acid (AJA) is a synthetic analog of THC-11-oic acid, a metabolite of tetrahydrocannabinol (THC), the major active ingredient of the recreational drug marijuana derived from the plant Cannabis sativa. AJA has potent analgesic and anti-inflammatory activity in vivo, but without the psychotropic action of THC. However, its precise mechanism of action remains unknown. Biochemical studies indicate that AJA binds directly and selectively to the isotype gamma of the peroxisome proliferator-activated receptor (PPARgamma) suggesting that this may be a pharmacologically relevant receptor for this compound and a potential target for drug development in the treatment of pain and inflammation. Here, we report the crystal structure of the ligand binding domain of the gamma isotype of human PPAR in complex with ajulemic acid, determined at 2.8-A resolution. Our results show a binding mode that is compatible with other known partial agonists of PPAR, explaining their moderate activation of the receptor, as well as the structural basis for isotype selectivity, as observed previously in vitro. The structure also provides clues to the understanding of partial agonism itself, suggesting a rational approach to the design of molecules capable of activating the receptor at levels that avoid undesirable side effects.

Insights into the mechanism of partial agonism: crystal structures of the peroxisome proliferator-activated receptor gamma ligand-binding domain in the complex with two enantiomeric ligands

The peroxisome proliferator-activated receptors (PPARs) are transcriptional regulators of glucose and lipid metabolism. They are activated by natural ligands, such as fatty acids, and are also targets of synthetic antidiabetic and hypolipidemic drugs. By using cell-based reporter assays, we studied the transactivation activity of two enantiomeric ureidofibrate-like derivatives. In particular, we show that the R-enantiomer, (R)-1, is a full agonist of PPARgamma, whereas the S-enantiomer, (S)-1, is a less potent partial agonist. Most importantly, we report the x-ray crystal structures of the PPARgamma ligand binding domain complexed with the R- and the S-enantiomer, respectively. The analysis of the two crystal structures shows that the different degree of stabilization of the helix 12 induced by the ligand determines its behavior as full or partial agonist. Another crystal structure of the PPARgamma.(S)-1 complex, only differing in the soaking time of the ligand, is also presented. The comparison of the two structures of the complexes with the partial agonist reveals significant differences and is suggestive of the possible coexistence in solution of transcriptionally active and inactive forms of helix 12 in the presence of a partial agonist. Mutation analysis confirms the importance of Leu(465), Leu(469), and Ile(472) in the activation by (R)-1 and underscores the key role of Gln(286) in the PPARgamma activity.

Advances in virtual screening

Although the term virtual screening as the in silico analog of high throughput screening has been coined only a decade ago, virtual screening is now a widespread lead identification method in the pharmaceutical industry. A myriad of different methods have been developed exploiting the growing library of target structures and assay data as a basis for finding new lead structures. Exploiting synergies between different methods best utilizes the information available and is at the center of recent developments.

Combined simulation and mutagenesis analyses reveal the involvement of key residues for peroxisome proliferator-activated receptor alpha helix 12 dynamic behavior

The dynamic properties of helix 12 in the ligand binding domain of nuclear receptors are a major determinant of AF-2 domain activity. We investigated the molecular and structural basis of helix 12 mobility, as well as the involvement of individual residues with regard to peroxisome proliferator-activated receptor alpha (PPARalpha) constitutive and ligand-dependent transcriptional activity. Functional assays of the activity of PPARalpha helix 12 mutants were combined with free energy molecular dynamics simulations. The agreement between the results from these approaches allows us to make robust claims concerning the mechanisms that govern helix 12 functions. Our data support a model in which PPARalpha helix 12 transiently adopts a relatively stable active conformation even in the absence of a ligand. This conformation provides the interface for the recruitment of a coactivator and results in constitutive activity. The receptor agonists stabilize this conformation and increase PPARalpha transcription activation potential. Finally, we disclose important functions of residues in PPARalpha AF-2, which determine the positioning of helix 12 in the active conformation in the absence of a ligand. Substitution of these residues suppresses PPARalpha constitutive activity, without changing PPARalpha ligand-dependent activation potential.

Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve.

Design and synthesis of novel N-sulfonyl-2-indole carboxamides as potent PPAR-γ binding agents with potential application to the treatment of osteoporosis

The synthesis and structure-activity relationships of a novel series of N-sulfonyl-2-indole carboxamides that bind to peroxisome proliferator-activated receptor gamma (PPAR-gamma) are reported. Chemical optimization of the series led to the identification of 4q (IC(50)=50 nM) as a potent binding agent of PPAR-gamma. Also reported is preliminary cell based data suggesting the use of these compounds in the treatment of osteoporosis.