Pharmacophore modeling and parallel screening for PPAR ligands

Selective PPAR-Delta/PPAR-Gamma Activation Improves Cognition in a Model of Alzheimer's Disease

Background: The continuously increasing association of Alzheimer's disease (AD) with increased mortality rates indicates an unmet medical need and the critical need for establishing novel molecular targets for therapeutic potential. Agonists for peroxisomal proliferator activating receptors (PPAR) are known to regulate energy in the body and have shown positive effects against Alzheimer's disease. There are three members of this class (delta, gamma, and alpha), with PPAR-gamma being the most studied, as these pharmaceutical agonists offer promise for AD because they reduce amyloid beta and tau pathologies, display anti-inflammatory properties, and improve cognition. However, they display poor brain bioavailability and are associated with several adverse side effects on human health, thus limiting their clinical application. Methods: We have developed a novel series of PPAR-delta and PPAR-gamma agonists in silico with AU9 as our lead compound that displays selective amino acid interactions focused upon avoiding the Tyr-473 epitope in the PPAR-gamma AF2 ligand binding domain. Results: This design helps to avoid the unwanted side effects of current PPAR-gamma agonists and improve behavioral deficits and synaptic plasticity while reducing amyloid-beta levels and inflammation in 3xTgAD animals. Conclusions: Our innovative in silico design of PPAR-delta/gamma agonists may offer new perspectives for this class of agonists for AD.

Discovery by Virtual Screening of an Inhibitor of CDK5-Mediated PPARγ Phosphorylation

Thiazolidinedione PPARγ agonists such as rosiglitazone and pioglitazone are effective antidiabetic drugs, but side effects have limited their use. It has been posited that their positive antidiabetic effects are mainly mediated by the inhibition of the CDK5-mediated Ser273 phosphorylation of PPARγ, whereas the side effects are linked to classical PPARγ agonism. Thus compounds that inhibit PPARγ Ser273 phosphorylation but lack classical PPARγ agonism have been sought as safer antidiabetic therapies. Herein we report the discovery by virtual screening of 10, which is a potent PPARγ binder and in vitro inhibitor of the CDK5-mediated phosphorylation of PPARγ Ser273 and displays negligible PPARγ agonism in a reporter gene assay. The pharmacokinetic properties of 10 are compatible with oral dosing, enabling preclinical in vivo testing, and a 7 day treatment demonstrated an improvement in insulin sensitivity in the ob/ob diabetic mouse model.

Multi-Target Drugs Against Metabolic Disorders

BACKGROUND

Metabolic disorders are a major cause of illness and death worldwide. Metabolism is the process by which the body makes energy from proteins, carbohydrates, and fats; chemically breaking these down in the digestive system towards sugars and acids which constitute the human body's fuel for immediate use, or to store in body tissues, such as the liver, muscles, and body fat.

OBJECTIVE

The efficiency of treatments for multifactor diseases has not been proved. It is accepted that to manage multifactor diseases, simultaneous modulation of multiple targets is required leading to the development of new strategies for discovery and development of drugs against metabolic disorders.

METHODS

In silico studies are increasingly being applied by researchers due to reductions in time and costs for new prototype synthesis; obtaining substances that present better therapeutic profiles.

DISCUSSION

In the present work, in addition to discussing multi-target drug discovery and the contributions of in silico studies to rational bioactive planning against metabolic disorders such as diabetes and obesity, we review various in silico study contributions to the fight against human metabolic pathologies.

CONCLUSION

In this review, we have presented various studies involved in the treatment of metabolic disorders; attempting to obtain hybrid molecules with pharmacological activity against various targets and expanding biological activity by using different mechanisms of action to treat a single pathology.

Discovery of peroxisome proliferator-activated receptor α (PPARα) activators with a ligand-screening system using a human PPARα-expressing cell line

Peroxisome proliferator-activated receptor α (PPARα) is a ligand-activated transcription factor that belongs to the superfamily of nuclear hormone receptors. PPARα is mainly expressed in the liver, where it activates fatty acid oxidation and lipoprotein metabolism and improves plasma lipid profiles. Therefore, PPARα activators are often used to treat patients with dyslipidemia. To discover additional PPARα activators as potential compounds for use in hypolipidemic drugs, here we established human hepatoblastoma cell lines with luciferase reporter expression from the promoters containing peroxisome proliferator-responsive elements (PPREs) and tetracycline-regulated expression of full-length human PPARα to quantify the effects of chemical ligands on PPARα activity. Using the established cell-based PPARα-activator screening system to screen a library of >12,000 chemical compounds, we identified several hit compounds with basic chemical skeletons different from those of known PPARα agonists. One of the hit compounds, a 1H-pyrazolo[3,4-b]pyridine-4-carboxylic acid derivative we termed compound 3, selectively up-regulated PPARα transcriptional activity, leading to PPARα target gene expression both in vitro and in vivo Of note, the half-maximal effective concentrations of the hit compounds were lower than that of the known PPARα ligand fenofibrate. Finally, fenofibrate or compound 3 treatment of high fructose-fed rats having elevated plasma triglyceride levels for 14 days indicated that compound 3 reduces plasma triglyceride levels with similar efficiency as fenofibrate. These observations raise the possibility that 1H-pyrazolo[3,4-b]pyridine-4-carboxylic acid derivatives might be effective drug candidates for selective targeting of PPARα to manage dyslipidemia.

Screening for PPAR Non-Agonist Ligands Followed by Characterization of a Hit, AM-879, with Additional No-Adipogenic and cdk5-Mediated Phosphorylation Inhibition Properties

Peroxisome proliferator-activated receptor gamma (PPARγ) is a member of a nuclear receptor superfamily and acts as a ligand-dependent transcription factor, playing key roles in maintenance of adipose tissue and in regulation of glucose and lipid homeostasis. This receptor is the target of thiazolidinediones, a class of antidiabetic drugs, which improve insulin sensitization and regulate glycemia in type 2 diabetes. Despite the beneficial effects of drugs, such as rosiglitazone and pioglitazone, their use is associated with several side effects, including weight gain, heart failure, and liver disease, since these drugs induce full activation of the receptor. By contrast, a promising activation-independent mechanism that involves the inhibition of cyclin-dependent kinase 5 (CDK5)-mediated PPARγ phosphorylation has been related to the insulin-sensitizing effects induced by these drugs. Thus, we aimed to identify novel PPARγ ligands that do not possess agonist properties by conducting a mini-trial with 80 compounds using the sequential steps of thermal shift assay, 8-anilino-1-naphthalenesulfonic acid fluorescence quenching, and a cell-based transactivation assay. We identified two non-agonist PPARγ ligands, AM-879 and P11, and one partial-agonist, R32. Using fluorescence anisotropy, we show that AM-879 does not dissociate the NCOR corepressor in vitro, and it has only a small effect on TRAP coactivator recruitment. In cells, AM-879 could not induce adipocyte differentiation or positively regulate the expression of genes associated with adipogenesis. In addition, AM-879 inhibited CDK5-mediated phosphorylation of PPARγ in vitro. Taken together, these findings supported an interaction between AM-879 and PPARγ; this interaction was identified by the analysis of the crystal structure of the PPARγ:AM-879 complex and evidenced by AM-879's mechanism of action as a putative PPARγ non-agonist with antidiabetic properties. Moreover, we present an optimized assay pipeline capable of detecting ligands that physically bind to PPARγ but do not cause its activation as a new strategy to identify ligands for this nuclear receptor.

Peroxisome proliferator-activated receptor α ligands and modulators from dietary compounds: Types, screening methods and functions

Peroxisome proliferator-activated receptor α (PPARα) plays a key role in lipid metabolism and glucose homeostasis and a crucial role in the prevention and treatment of metabolic diseases. Natural dietary compounds, including nutrients and phytochemicals, are PPARα ligands or modulators. High-throughput screening assays have been developed to screen for PPARα ligands and modulators in our diet. In the present review, we discuss recent advances in our knowledge of PPARα, including its structure, function, and ligand and modulator screening assays, and summarize the different types of dietary PPARα ligands and modulators.

Synthesis and biological evaluation of dihydropyrano-[2,3-c]pyrazoles as a new class of PPARγ partial agonists

Peroxisome proliferator-activated receptor γ (PPARγ) is a well-known target for thiazolidinedione antidiabetic drugs. In this paper, we present the synthesis and biological evaluation of a series of dihydropyrano[2,3-c]pyrazole derivatives as a novel family of PPARγ partial agonists. Two analogues were found to display high affinity for PPARγ with potencies in the micro molar range. Both of these hits were selective against PPARγ, since no activity was measured when tested against PPARα, PPARδ and RXRα. In addition, a novel modelling approach based on multiple individual flexible alignments was developed for the identification of ligand binding interactions in PPARγ. In combination with cell-based transactivation experiments, the flexible alignment model provides an excellent analytical tool to evaluate and visualize the effect of ligand chemical structure with respect to receptor binding mode and biological activity.

Probing the intermolecular interactions of PPARγ-LBD with polyunsaturated fatty acids and their anti-inflammatory metabolites to infer most potential binding moieties

Background

PPARγ is an isoform of peroxisome proliferator-activated receptor (PPAR) belonging to a super family of nuclear receptors. PPARγ receptor is found to play a crucial role in the modulation of lipid and glucose homeostasis. Its commotion has been reported to play a significant role in a broad spectrum of diseases such as type 2 diabetes mellitus, inflammatory diseases, Alzheimer’s disease, and in some cancers. Hence, PPARγ is an important therapeutic target. Polyunsaturated fatty acids (PUFAs) and their metabolites (henceforth referred to as bioactive lipids) are known to function as agonists of PPARγ. However, agonistic binding modes and affinity of these ligands to PPARγ are yet to be deciphered.

Methods

In this study, we performed a comparative molecular docking, binding free energy calculation and molecular dynamics simulation to infer and rank bioactive lipids based on the binding affinities with the ligand binding domain (LBD) of PPARγ.

Results

The results inferred affinity in the order of resolvin E1 > neuroprotectin D1 > hydroxy-linoleic acid > docosahexaenoic acid > lipoxin A4 > gamma-linolenic acid, arachidonic acid > alpha-linolenic acid > eicosapentaenoic acid > linoleic acid. Of all the bioactive lipids studied, resolvin E1, neuroprotectin D1 and hydroxy-linoleic acid showed significant affinity comparable to proven PPARγ agonist namely, rosiglitazone, in terms of Glide XP docking score, H-bond formation with the key residues, binding free energy and stable complex formation with LBD favouring co-activator binding, as inferred through Molecular Dynamics trajectory analysis.

Conclusion

Hence, these three bioactive lipids (resolvin E1, neuroprotectin D1 and hydroxy-linoleic acid) may be favourably considered as ideal drug candidates in therapeutic modulation of clinical conditions such as type 2 DM, Alzheimer’s disease and other instances where PPARγ is a key player.

Mitochondrial activation chemicals synergize with surface receptor PD-1 blockade for T cell-dependent antitumor activity

Although immunotherapy by PD-1 blockade has dramatically improved the survival rate of cancer patients, further improvement in efficacy is required to reduce the fraction of less sensitive patients. In mouse models of PD-1 blockade therapy, we found that tumor-reactive cytotoxic T lymphocytes (CTLs) in draining lymph nodes (DLNs) carry increased mitochondrial mass and more reactive oxygen species (ROS). We show that ROS generation by ROS precursors or indirectly by mitochondrial uncouplers synergized the tumoricidal activity of PD-1 blockade by expansion of effector/memory CTLs in DLNs and within the tumor. These CTLs carry not only the activation of mechanistic target of rapamycin (mTOR) and AMP-activated protein kinase (AMPK) but also an increment of their downstream transcription factors such as PPAR-gamma coactivator 1α (PGC-1α) and T-bet. Furthermore, direct activators of mTOR, AMPK, or PGC-1α also synergized the PD-1 blockade therapy whereas none of above-mentioned chemicals alone had any effects on tumor growth. These findings will pave a way to developing novel combinatorial therapies with PD-1 blockade.

Bioinformatics and Its Therapeutic Applications

Bioinformatics has emerged as a major element in contemporary biomedical and pharmaceutical region. Bioinformatics deals with growth in biological data and has led to development of many databases. Bioinformatics deals with collection of data that is relevant clinically and these days separate term clinical information has come up. Data mimics are another field which is gaining importance. This chapter shall deal with introduction of bioinformatics and its applications in medicine and health care.

Predicting Cyclooxygenase Inhibition by Three-Dimensional Pharmacophoric Profiling. Part I: Model Generation, Validation and Applicability in Ethnopharmacology

3D pharmacophore modeling has evolved as an established and state-of-the-art method for performing in-silico predictions of biological activity. Using one single model is limited to single binding modes, while the combination of several models bears a broader application scope. We demonstrate the generation of a complete and predictive 3D model set for cyclooxygenase 1 and 2 inhibitors, along with a selection and validation protocol optimized for parallel virtual screening. This model set was applied to explain the cyclooxygenase activity of an ethnopharmacologically known mixture of natural products, the Thai traditional medicine "Prasaplai". Results show that rationalizing natural product activity by modern in-silico approaches is promising and can be tremendously useful in the identification of the mechanisms of action for known biological effects of complex herbal remedies.

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines, as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites, such as polysaccharides, or secondary metabolites, such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e., chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, the focus was laid on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level.

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.

Anthranilic acid derivatives as nuclear receptor modulators--development of novel PPAR selective and dual PPAR/FXR ligands

Nuclear receptors, especially the peroxisome proliferator activated receptors (PPARs) and the farnesoid X receptor (FXR) fulfill crucial roles in metabolic balance. Their activation offers valuable therapeutic potential which has high clinical relevance with the fibrates and glitazones as PPAR agonistic drugs. With growing knowledge about the various functions of nuclear receptors in many disorders, new selective or dual ligands of these pharmaceutical targets are however still required. Here we report the class of anthranilic acid derivatives as novel selective PPAR or dual FXR/PPAR ligands. We identified distinct molecular determinants that govern selectivity for each PPAR subtype or FXR as well as the amplitude of activation of the respective receptors. We thereby discovered several lead compounds for further optimization and developed a highly potent dual PPARα/FXR partial agonist that might have a beneficial synergistic effect on lipid homeostasis by simultaneous activation of two nuclear receptors involved in lipid metabolism.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.

trans-Caryophyllene is a natural agonistic ligand for peroxisome proliferator-activated receptor-α

Intake of dietary aroma compounds may regulate cellular lipid metabolism. We demonstrated that trans-caryophyllene, a flavor compound in plant foods and teas, activates peroxisome proliferator-activated receptor (PPAR)-α through direct interaction with the ligand-binding domain of PPAR-α. The agonistic activity of trans-caryophyllene was investigated by the luciferase reporter assay, surface plasmon resonance, and time-resolved fluorescence resonance energy transfer assay. Following the stimulation of cells with trans-caryophyllene, intracellular triglyceride concentrations were significantly reduced by 17%, and hepatic fatty acid uptake was significantly increased by 31%. The rate of fatty acid oxidation was also significantly increased. The expressions of PPAR-α and its target genes and proteins in fatty acid uptake and oxidation were significantly up-regulated as well. In HepG2 cells transfected with small interfering RNA of PPAR-α, the effects of trans-caryophyllene on PPAR-α responsive gene expressions, intracellular triglyceride, fatty acid uptake and oxidation were disappeared. These results indicate that the aroma compound, trans-caryophyllene, is PPAR-α agonist thus regulates cellular lipid metabolism in PPAR-α dependent manners.

Type I interferon-mediated pathway interacts with peroxisome proliferator activated receptor-γ (PPAR-γ): at the cross-road of pancreatic cancer cell proliferation

Pancreatic adenocarcinoma remains an unresolved therapeutic challenge because of its intrinsically refractoriness to both chemo- and radiotherapy due to the complexity of signaling and the activation of survival pathways in cancer cells. Recent studies have demonstrated that the combination of some drugs, targeting most of aberrant pathways crucial for the survival of pancreatic cancer cells may be a valid antitumor strategy for this cancer. Type I interferons (IFNs) may have a role in the pathogenesis and progression of pancreatic adenocarcinoma, but the limit of their clinical use is due to the activation of tumor resistance mechanisms, including JAK-2/STAT-3 pathway. Moreover, aberrant constitutive activation of STAT-3 proteins has been frequently detected in pancreatic adenocarcinoma. The selective targeting of these cell survival cascades could be a promising strategy in order to enhance the antitumor effects of type I IFNs. The activation of peroxisome proliferator-activated receptor γ (PPAR-γ), on the other hand, has a suppressive activity on STAT-3. In fact, PPAR-γ agonists negatively modulate STAT-3 through direct and/or indirect mechanisms in several normal and cancer models. This review provides an overview on the current knowledge about the molecular mechanisms and antitumor activity of these two promising classes of drugs for pancreatic cancer therapy. Finally, the synergistic antiproliferative activity of combined IFN-β and troglitazone treatment on pancreatic cancer cell lines, evaluated in vitro, and the consequent potential clinical applications will be discussed.

Honokiol: a non-adipogenic PPARγ agonist from nature

Background

Peroxisome proliferator-activated receptor gamma (PPARγ) agonists are clinically used to counteract hyperglycemia. However, so far experienced unwanted side effects, such as weight gain, promote the search for new PPARγ activators.

Methods

We used a combination of in silico, in vitro, cell-based and in vivo models to identify and validate natural products as promising leads for partial novel PPARγ agonists.

Results

The natural product honokiol from the traditional Chinese herbal drug Magnolia bark was in silico predicted to bind into the PPARγ ligand binding pocket as dimer. Honokiol indeed directly bound to purified PPARγ ligand-binding domain (LBD) and acted as partial agonist in a PPARγ-mediated luciferase reporter assay. Honokiol was then directly compared to the clinically used full agonist pioglitazone with regard to stimulation of glucose uptake in adipocytes as well as adipogenic differentiation in 3T3-L1 pre-adipocytes and mouse embryonic fibroblasts. While honokiol stimulated basal glucose uptake to a similar extent as pioglitazone, it did not induce adipogenesis in contrast to pioglitazone. In diabetic KKAy mice oral application of honokiol prevented hyperglycemia and suppressed weight gain.

Conclusion

We identified honokiol as a partial non-adipogenic PPARγ agonist in vitro which prevented hyperglycemia and weight gain in vivo.

General significance

This observed activity profile suggests honokiol as promising new pharmaceutical lead or dietary supplement to combat metabolic disease, and provides a molecular explanation for the use of Magnolia in traditional medicine.

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Honokiol is identified and characterized as novel partial PPARγ agonist from nature.

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In cell models honokiol increases glucose uptake but is not adipogenic.

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In KKAy diabetic mice it decreases blood glucose and suppresses weight gain.

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PPARγ agonism of honokiol may explain the use of Magnolia bark in traditional medicine.

Find novel dual-agonist drugs for treating type 2 diabetes by means of cheminformatics

The high prevalence of type 2 diabetes mellitus in the world as well as the increasing reports about the adverse side effects of the existing diabetes treatment drugs have made developing new and effective drugs against the disease a very high priority. In this study, we report ten novel compounds found by targeting peroxisome proliferator-activated receptors (PPARs) using virtual screening and core hopping approaches. PPARs have drawn increasing attention for developing novel drugs to treat diabetes due to their unique functions in regulating glucose, lipid, and cholesterol metabolism. The reported compounds are featured with dual functions, and hence belong to the category of dual agonists. Compared with the single PPAR agonists, the dual PPAR agonists, formed by combining the lipid benefit of PPARα agonists (such as fibrates) and the glycemic advantages of the PPARγ agonists (such as thiazolidinediones), are much more powerful in treating diabetes because they can enhance metabolic effects while minimizing the side effects. This was observed in the studies on molecular dynamics simulations, as well as on absorption, distribution, metabolism, and excretion, that these novel dual agonists not only possessed the same function as ragaglitazar (an investigational drug developed by Novo Nordisk for treating type 2 diabetes) did in activating PPARα and PPARγ, but they also had more favorable conformation for binding to the two receptors. Moreover, the residues involved in forming the binding pockets of PPARα and PPARγ among the top ten compounds are explicitly presented, and this will be very useful for the in-depth conduction of mutagenesis experiments. It is anticipated that the ten compounds may become potential drug candidates, or at the very least, the findings reported here may stimulate new strategies or provide useful insights for designing new and more powerful dual-agonist drugs for treating type 2 diabetes.

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.

Design and synthesis of marine fungal phthalide derivatives as PPAR-γ agonists

On the basis of a marine fungal phthalide (paecilocin A) skeleton, we synthesized 20 analogs and evaluated them for peroxisome proliferator-activated receptor gamma (PPAR-γ) binding and activation. Among these analogs, 6 and 7 had significant PPAR-γ binding activity, and 7 showed further PPAR-γ activation in rat liver Ac2F cells. In docking simulation, 7 formed H bonds with key amino acid residues of the PPAR-γ binding domain, and the overall positioning was similar to rosiglitazone. This new phthalide derivative is considered an interesting new molecular class of PPAR-γ ligands.

Design novel dual agonists for treating type-2 diabetes by targeting peroxisome proliferator-activated receptors with core hopping approach

Owing to their unique functions in regulating glucose, lipid and cholesterol metabolism, PPARs (peroxisome proliferator-activated receptors) have drawn special attention for developing drugs to treat type-2 diabetes. By combining the lipid benefit of PPAR-alpha agonists (such as fibrates) with the glycemic advantages of the PPAR-gamma agonists (such as thiazolidinediones), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of the powerful “core hopping” and “glide docking” techniques, a novel class of PPAR dual agonists was discovered based on the compound GW409544, a well-known dual agonist for both PPAR-alpha and PPAR-gamma modified from the farglitazar structure. It was observed by molecular dynamics simulations that these novel agonists not only possessed the same function as GW409544 did in activating PPAR-alpha and PPAR-gamma, but also had more favorable conformation for binding to the two receptors. It was further validated by the outcomes of their ADME (absorption, distribution, metabolism, and excretion) predictions that the new agonists hold high potential to become drug candidates. Or at the very least, the findings reported here may stimulate new strategy or provide useful insights for discovering more effective dual agonists for treating type-2 diabetes. Since the “core hopping” technique allows for rapidly screening novel cores to help overcome unwanted properties by generating new lead compounds with improved core properties, it has not escaped our notice that the current strategy along with the corresponding computational procedures can also be utilized to find novel and more effective drugs for treating other illnesses.

Structural insights into human peroxisome proliferator activated receptor delta (PPAR-delta) selective ligand binding

Peroxisome proliferator activated receptors (PPARs δ, α and γ) are closely related transcription factors that exert distinct effects on fatty acid and glucose metabolism, cardiac disease, inflammatory response and other processes. Several groups developed PPAR subtype specific modulators to trigger desirable effects of particular PPARs without harmful side effects associated with activation of other subtypes. Presently, however, many compounds that bind to one of the PPARs cross-react with others and rational strategies to obtain highly selective PPAR modulators are far from clear. GW0742 is a synthetic ligand that binds PPARδ more than 300-fold more tightly than PPARα or PPARγ but the structural basis of PPARδ:GW0742 interactions and reasons for strong selectivity are not clear. Here we report the crystal structure of the PPARδ:GW0742 complex. Comparisons of the PPARδ:GW0742 complex with published structures of PPARs in complex with α and γ selective agonists and pan agonists suggests that two residues (Val312 and Ile328) in the buried hormone binding pocket play special roles in PPARδ selective binding and experimental and computational analysis of effects of mutations in these residues confirms this and suggests that bulky substituents that line the PPARα and γ ligand binding pockets as structural barriers for GW0742 binding. This analysis suggests general strategies for selective PPARδ ligand design.

Computational tools for polypharmacology and repurposing

Most drugs act on a multitude of targets rather than on one single target. Polypharmacology, an upcoming branch of pharmaceutical science, deals with the recognition of these off-target activities of small chemical compounds. Due to the high amount of data to be processed, application of computational methods is indispensable in this area. This review summarizes the most important in silico approaches for polypharmacology. The described methods comprise network pharmacology, machine learning techniques and chemogenomic approaches. The use of these methods for drug repurposing as a branch of drug discovery and development is discussed. Furthermore, a broad range of prospective applications is summarized to give the reader an overview of possibilities and limitations of the described techniques.

Dietary α-eleostearic acid ameliorates experimental inflammatory bowel disease in mice by activating peroxisome proliferator-activated receptor-γ

Background

Treatments for inflammatory bowel disease (IBD) are modestly effective and associated with side effects from prolonged use. As there is no known cure for IBD, alternative therapeutic options are needed. Peroxisome proliferator-activated receptor-gamma (PPARγ) has been identified as a potential target for novel therapeutics against IBD. For this project, compounds were screened to identify naturally occurring PPARγ agonists as a means to identify novel anti-inflammatory therapeutics for experimental assessment of efficacy.

Methodology/Principal Findings

Here we provide complementary computational and experimental methods to efficiently screen for PPARγ agonists and demonstrate amelioration of experimental IBD in mice, respectively. Computational docking as part of virtual screening (VS) was used to test binding between a total of eighty-one compounds and PPARγ. The test compounds included known agonists, known inactive compounds, derivatives and stereoisomers of known agonists with unknown activity, and conjugated trienes. The compound identified through VS as possessing the most favorable docked pose was used as the test compound for experimental work. With our combined methods, we have identified α-eleostearic acid (ESA) as a natural PPARγ agonist. Results of ligand-binding assays complemented the screening prediction. In addition, ESA decreased macrophage infiltration and significantly impeded the progression of IBD-related phenotypes through both PPARγ-dependent and –independent mechanisms in mice with experimental IBD.

Conclusions/Significance

This study serves as the first significant step toward a large-scale VS protocol for natural PPARγ agonist screening that includes a massively diverse ligand library and structures that represent multiple known target pharmacophores.

Pharmacophore-driven identification of PPARγ agonists from natural sources

In a search for more effective and safe anti-diabetic compounds, we developed a pharmacophore model based on partial agonists of PPARγ. The model was used for the virtual screening of the Chinese Natural Product Database (CNPD), a library of plant-derived natural products primarily used in folk medicine. From the resulting hits, we selected methyl oleanonate, a compound found, among others, in Pistacia lentiscus var. Chia oleoresin (Chios mastic gum). The acid of methyl oleanonate, oleanonic acid, was identified as a PPARγ agonist through bioassay-guided chromatographic fractionations of Chios mastic gum fractions, whereas some other sub-fractions exhibited also biological activity towards PPARγ. The results from the present work are two-fold: on the one hand we demonstrate that the pharmacophore model we developed is able to select novel ligand scaffolds that act as PPARγ agonists; while at the same time it manifests that natural products are highly relevant for use in virtual screening-based drug discovery.

Characterization of new PPARgamma agonists: benzimidazole derivatives-importance of positions 5 and 6, and computational studies on the binding mode

In this and previous studies we investigated the importance of partial structures of Telmisartan on PPARgamma activation. The biphenyl-4-ylmethyl moiety at N1 and residues at C2 of the central benzimidazole were identified to be essential for receptor activation and potency of receptor binding. Now we focused our attention on positions 5 and 6 of the central benzimidazole and introduced bromine (3b-5/6, 3c), phenylcarbonyl (3d-5/6), hydroxy(phenyl)methyl (3g-5/6), hydroxymethyl (3h-5/6) and formyl (3i) groups. The selection of these moieties was inspired by the structure of Losartan and its metabolite EXP3179. In order to increase the hydrophobicity of the central part of the molecule, the benzimidazole was exchanged by a naphtho[2,3-d]imidazole (5). The compounds 3a-3i and 5 were tested in a differentiation assay using 3T3-L1 preadipocytes and a luciferase assay using COS-7 cells, transiently transfected with pGal4-hPPARgammaDEF, pGal5-TK-pGL3 and pRL-CMV, as established models for the assessment of cellular PPARgamma activation. An enhanced effect on PPARgamma activation could be observed if lipophilic moieties are introduced in these positions. 4'-[(2-Propyl-1H-naphtho[2,3-d]imidazol-1-yl)methyl]biphenyl-2-carboxylic acid (5) was identified as the most potent compound with an EC(50) of 0.26muM and the profile of a full agonist. Together with compounds of the former structure-activity relationship study (position 2-substituted benzimidazole derivatives 4a-4j), the binding mode of Telmisartan and its derivatives have been analyzed in 3D pharmacophore-driven docking experiments.

Structure-Based Approaches to Target Fishing and Ligand Profiling

Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time-consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure-based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

Computer-aided discovery, validation, and mechanistic characterization of novel neolignan activators of peroxisome proliferator-activated receptor gamma

Peroxisome proliferator-activated receptor gamma (PPAR gamma) agonists are used for the treatment of type 2 diabetes and metabolic syndrome. However, the currently used PPAR gamma agonists display serious side effects, which has led to a great interest in the discovery of novel ligands with favorable properties. The aim of our study was to identify new PPARgamma agonists by a PPAR gamma pharmacophore-based virtual screening of 3D natural product libraries. This in silico approach led to the identification of several neolignans predicted to bind the receptor ligand binding domain (LBD). To confirm this prediction, the neolignans dieugenol, tetrahydrodieugenol, and magnolol were isolated from the respective natural source or synthesized and subsequently tested for PPAR gamma receptor binding. The neolignans bound to the PPAR gamma LBD with EC(50) values in the nanomolar range, exhibiting a binding pattern highly similar to the clinically used agonist pioglitazone. In intact cells, dieugenol and tetrahydrodieugenol selectively activated human PPAR gamma-mediated, but not human PPAR alpha- or -beta/delta-mediated luciferase reporter expression, with a pattern suggesting partial PPAR gamma agonism. The coactivator recruitment study also demonstrated partial agonism of the tested neolignans. Dieugenol, tetrahydrodieugenol, and magnolol but not the structurally related eugenol induced 3T3-L1 preadipocyte differentiation, confirming effectiveness in a cell model with endogenous PPAR gamma expression. In conclusion, we identified neolignans as novel ligands for PPAR gamma, which exhibited interesting activation profiles, recommending them as potential pharmaceutical leads or dietary supplements.

Targets looking for drugs: a multistep computational protocol for the development of structure-based pharmacophores and their applications for hit discovery

Pharmacophoresthree-dimensional (3D) arrangements of essential features enabling a molecule to exert a particular biological effectconstitute a very useful tool in drug design both in hit discovery and hit-to-lead optimization process. Two basic approaches for pharmacophoric model generation can be used by chemists, depending on the availability or not of the target 3D structure. In view of the rapidly growing number of protein structures that are now available, receptor-based pharmacophore generation methods are becoming more and more used. Since most of them require the knowledge of the 3D structure of the ligand-target complex, they cannot be applied when no compounds targeting the binding site of interest are known. Here, a GRID-based procedure for the generation of receptor-based pharmacophores starting from the knowledge of the sole protein structure is described and successfully applied to address three different tasks in the field of medicinal chemistry.

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.