Identification of an N-oxide pyridine GW4064 analog as a potent FXR agonist

Mechanistic Impact of Different Ligand Scaffolds on FXR Modulation Suggests Avenues to Selective Modulators

The bile-acid sensing nuclear farnesoid X receptor (FXR) is an attractive target for the treatment of hepatic and metabolic diseases, but application of this chemotherapeutic concept remains limited due to adverse effects of FXR activation observed in clinical trials. To elucidate the mechanistic basis of FXR activation at the molecular level, we have systematically studied FXR co-regulator interactions and dimerization in response to seven chemically diverse FXR ligands. Different molecular effects on FXR activation mediated by different scaffolds were evident and aligned with characteristic structural changes within the ligand binding domain of FXR. A partial FXR agonist acted mainly through co-repressor displacement from FXR and caused an FXR-regulated gene expression pattern markedly differing from FXR agonist effects. These results suggest selective modulation of FXR dimerization and co-regulator interactions for different ligands, offering a potential avenue for the design of gene- or tissue-selective FXR modulators.

Emerging targets and potential therapeutic agents in non-alcoholic fatty liver disease treatment

Nonalcoholic Fatty Liver Disease (NAFLD) is the most common chronic liver disease in the world, which is characterized by liver fat accumulation unrelated to excessive drinking. Indeed, it attracts growing attention and becomes a global health problem. Due to the complexity of the NAFLD pathogenic mechanism, no related drugs were approved by Food and Drug Administration (FDA) till now. However, it is encouraging that a series of candidate drugs have entered the clinical trial stage with expectation to treat NAFLD. In this review, we summarized the main pathways and pathogenic mechanisms of NAFLD, as well as introduced the main potential therapeutic targets and the corresponding compounds involved in metabolism, inflammation and fibrosis. Furthermore, we also discuss the progress of these compounds, such as drug design and optimization, the choice of pharmacological properties and druglikeness, and the analysis of structure-activity relationship. This review offers a medium on future drug design and development, to be beneficial to relevant studies.

Bile-ology: from bench to bedside

Bile acids (BAs) are originally known as detergents essential for the digestion and absorption of lipids. In recent years, extensive research has unveiled new functions of BAs as gut hormones that modulate physiological and pathological processes, including glucose and lipid metabolism, energy expenditure, inflammation, tumorigenesis, cardiovascular disease, and even the central nervous system in addition to cholesterol homeostasis, enterohepatic protection and liver regeneration. BAs are closely linked with gut microbiota which might explain some of their crucial roles in organs. The signaling actions of BAs can also be mediated through specific nuclear receptors and membrane-bound G protein-coupled receptors. Several pharmacological agents or bariatric surgeries have demonstrated efficacious therapeutic effects on metabolic diseases through targeting BA signaling. In this mini-review, we summarize recent advances in bile-ology, focusing on its translational studies.

Discovery of Novel Molecular Frameworks of Farnesoid X Receptor Modulators by Ensemble Machine Learning

The bile acid activated transcription factor farnesoid X receptor (FXR) has revealed therapeutic potential as a molecular drug target for the treatment of hepatic and metabolic disorders. Despite strong efforts in FXR ligand development, the structural diversity among the known FXR modulators is limited. Only four molecular frameworks account for more than 50 % of the FXR modulators annotated in ChEMBL. Here, we leverage machine learning methods to expand the chemical space of FXR-targeting small molecules by employing an ensemble of three complementary machine learning approaches. A counter-propagation artificial neural network, a k-nearest neighbor learner, and a three-dimensional pharmacophore descriptor were combined to retrieve novel FXR ligands from a collection of more than 3 million compounds. The ensemble machine learning model identified six new FXR modulators among ten top-ranked candidates. These active hits comprise both FXR activators and antagonists with micromolar potencies. With four novel FXR ligand scaffolds, these computationally identified bioactive compounds appreciably expand the chemical space of known FXR modulators and may serve as starting points for hit-to-lead expansion.

Binding free energy predictions of farnesoid X receptor (FXR) agonists using a linear interaction energy (LIE) approach with reliability estimation: application to the D3R Grand Challenge 2

Computational protein binding affinity prediction can play an important role in drug research but performing efficient and accurate binding free energy calculations is still challenging. In the context of phase 2 of the Drug Design Data Resource (D3R) Grand Challenge 2 we used our automated eTOX ALLIES approach to apply the (iterative) linear interaction energy (LIE) method and we evaluated its performance in predicting binding affinities for farnesoid X receptor (FXR) agonists. Efficiency was obtained by our pre-calibrated LIE models and molecular dynamics (MD) simulations at the nanosecond scale, while predictive accuracy was obtained for a small subset of compounds. Using our recently introduced reliability estimation metrics, we could classify predictions with higher confidence by featuring an applicability domain (AD) analysis in combination with protein-ligand interaction profiling. The outcomes of and agreement between our AD and interaction-profile analyses to distinguish and rationalize the performance of our predictions highlighted the relevance of sufficiently exploring protein-ligand interactions during training and it demonstrated the possibility to quantitatively and efficiently evaluate if this is achieved by using simulation data only.

D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank ( http://www.pdb.org ), and in affinity ranking and scoring of bound ligands.

Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

Modern drug design: the implication of using artificial neuronal networks and multiple molecular dynamic simulations

We report the implementation of molecular modeling approaches developed as a part of the 2016 Grand Challenge 2, the blinded competition of computer aided drug design technologies held by the D3R Drug Design Data Resource ( https://drugdesigndata.org/ ). The challenge was focused on the ligands of the farnesoid X receptor (FXR), a highly flexible nuclear receptor of the cholesterol derivative chenodeoxycholic acid. FXR is considered an important therapeutic target for metabolic, inflammatory, bowel and obesity related diseases (Expert Opin Drug Metab Toxicol 4:523-532, 2015), but in the context of this competition it is also interesting due to the significant ligand-induced conformational changes displayed by the protein. To deal with these conformational changes we employed multiple simulations of molecular dynamics (MD). Our MD-based protocols were top-ranked in estimating the free energy of binding of the ligands and FXR protein. Our approach was ranked second in the prediction of the binding poses where we also combined MD with molecular docking and artificial neural networks. Our approach showed mediocre results for high-throughput scoring of interactions.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and [Formula: see text]-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 [Formula: see text]. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 [Formula: see text] for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For [Formula: see text]-dynamics techniques, including multisite [Formula: see text]-dynamics (MS[Formula: see text]D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

Role of farnesoid X receptor in cholestasis

The nuclear receptor farnesoid X receptor (FXR) plays an important role in physiological bile acid synthesis, secretion and transport. Defects of FXR regulation in these processes can cause cholestasis and subsequent pathological changes. FXR regulates the synthesis and uptake of bile acid via enzymes. It also increases bile acid solubility and elimination by promoting conjugation reactions and exports pump expression in cholestasis. The changes in bile acid transporters are involved in cholestasis, which can result from the mutations of transporter genes or acquired dysfunction of transport systems, such as inflammation-induced intrahepatic cholestasis. The modulation function of FXR in extrahepatic cholestasis is not identical to that in intrahepatic cholestasis, but the discrepancy may be reduced over time. In extrahepatic cholestasis, increasing biliary pressure can induce bile duct proliferation and bile infarcts, but the absence of FXR may ameliorate them. This review provides an update on the function of FXR in the regulation of bile acid metabolism, its role in the pathophysiological process of cholestasis and the therapeutic use of FXR agonists.

NSAIDs Ibuprofen, Indometacin, and Diclofenac do not interact with Farnesoid X Receptor

The nuclear farnesoid X receptor (FXR) is a ligand activated transcription factor and acts as cellular sensor for bile acids. In this role, FXR is a highly important liver protector and FXR inhibition by antagonists or knockout has shown several deleterious effects. A recent report characterized non-steroidal anti-rheumatic drugs (NSAIDs) such as ibuprofen or diclofenac as FXR antagonists and linked hepatotoxic effects of these drugs with antagonistic activity on FXR. Since this would guide a way to develop safer anti-inflammatory agents by sparing FXR, we intended to further characterize the reported antagonistic activity and intensively investigated ibuprofen, indometacin and diclofenac. However, we conclude that these agents do not interact with FXR and that the reported reduced FXR signaling induced by CDCA in presence of NSAIDs is merely a consequence than a cause of hepatotoxicity.

Fragmentation of GW4064 led to a highly potent partial farnesoid X receptor agonist with improved drug-like properties

The ligand activated transcription factor farnesoid X receptor (FXR) is a crucial regulator of several metabolic and inflammatory pathways and its activation by agonistic ligands seems a valuable therapeutic approach for many disorders. Most known non-steroidal FXR agonists however, have limitations that hinder their clinical development and novel FXR ligands are required. Evaluation of the co-crystal structures of the widely used FXR agonist GW4064 and related compounds in complex with the FXR ligand binding domain indicated that their disubstituted isoxazole moiety is especially relevant for FXR activation. By investigation of GW4064-fragments missing the aromatic tail, we discovered a highly potent and soluble partial FXR agonist (14, ST-1892) as well as a fluorescent FXR ligand (15) as potential pharmacological tool.

Discovery and SAR study of hydroxyacetophenone derivatives as potent, non-steroidal farnesoid X receptor (FXR) antagonists

Compound 1 (IC50=35.2 ± 7.2 μM), a moderate FXR antagonist was discovered via high-throughput screening. Structure-activity relationship studies indicated that the shape and the lipophilicity of the substituents of the aromatic ring affect the activity dramatically, increasing the shape and the lipophilicity of the substituents of the aromatic ring enhances the potency of FXR antagonists. Especially, when the OH at C2 position of the aromatic ring was replaced by the OBn substituent (analog 2b), its activity could be improved to IC50=1.1 ± 0.1μM. Besides, the length of the linker and the tetrazole structure are essential for retaining the activity.

FXR signaling in the enterohepatic system

Enterohepatic circulation serves to capture bile acids and other steroid metabolites produced in the liver and secreted to the intestine, for reabsorption back into the circulation and reuptake to the liver. This process is under tight regulation by nuclear receptor signaling. Bile acids, produced from cholesterol, can alter gene expression in the liver and small intestine via activating the nuclear receptors farnesoid X receptor (FXR; NR1H4), pregnane X receptor (PXR; NR1I2), vitamin D receptor (VDR; NR1I1), G protein coupled receptor TGR5, and other cell signaling pathways (JNK1/2, AKT and ERK1/2). Among these controls, FXR is known to be a major bile acid-responsive ligand-activated transcription factor and a crucial control element for maintaining bile acid homeostasis. FXR has a high affinity for several major endogenous bile acids, notably cholic acid, deoxycholic acid, chenodeoxycholic acid, and lithocholic acid. By responding to excess bile acids, FXR is a bridge between the liver and small intestine to control bile acid levels and regulate bile acid synthesis and enterohepatic flow. FXR is highly expressed in the liver and gut, relative to other tissues, and contributes to the maintenance of cholesterol/bile acid homeostasis by regulating a variety of metabolic enzymes and transporters. FXR activation also affects lipid and glucose metabolism, and can influence drug metabolism.

Medicinal chemistry of farnesoid X receptor ligands: from agonists and antagonists to modulators

The nuclear receptor farnesoid X receptor (FXR) has emerged as a highly promising target in preclinical development in recent years. A significant amount of research has been conducted and, although none has reached clinical use, many synthetic ligands of FXR have been described. This review outlines the available knowledge regarding the medicinal chemistry and SAR of these FXR ligands, and discusses the molecular interactions of the compounds with the FXR ligand-binding domain by interpreting the existing co-crystal structures.

Conicasterol E, a small heterodimer partner sparing farnesoid X receptor modulator endowed with a pregnane X receptor agonistic activity, from the marine sponge Theonella swinhoei

We report the isolation and pharmacological characterization of conicasterol E isolated from the marine sponge Theonella swinhoei. Pharmacological characterization of this steroid in comparison to CDCA, a natural FXR ligand, and 6-ECDCA, a synthetic FXR agonist generated by an improved synthetic strategy, and rifaximin, a potent PXR agonist, demonstrated that conicasterol E is an FXR modulator endowed with PXR agonistic activity. Conicasterol E induces the expression of genes involved in bile acids detoxification without effect on the expression of small heterodimer partner (SHP), thus sparing the expression of genes involved in bile acids biosynthesis. The relative positioning in the ligand binding domain of FXR, explored through docking calculations, demonstrated a different spatial arrangement for conicasterol E and pointed to the presence of simultaneous and efficient interactions with the receptor. In summary, conicasterol E represents a FXR modulator and PXR agonist that might hold utility in treatment of liver disorders.

Pharmacophore-based discovery of FXR agonists. Part I: Model development and experimental validation

The farnesoid X receptor (FXR) is involved in glucose and lipid metabolism regulation, which makes it an attractive target for the metabolic syndrome, dyslipidemia, atherosclerosis, and type 2 diabetes. In order to find novel FXR agonists, a structure-based pharmacophore model collection was developed and theoretically evaluated against virtual databases including the ChEMBL database. The most suitable models were used to screen the National Cancer Institute (NCI) database. Biological evaluation of virtual hits led to the discovery of a novel FXR agonist with a piperazine scaffold (compound 19) that shows comparable activity as the endogenous FXR agonist chenodeoxycholic acid (CDCA, compound 2).

Pharmacophore-based discovery of FXR-agonists. Part II: identification of bioactive triterpenes from Ganoderma lucidum

The farnesoid X receptor (FXR) belonging to the metabolic subfamily of nuclear receptors is a ligand-induced transcriptional activator. Its central function is the physiological maintenance of bile acid homeostasis including the regulation of glucose and lipid metabolism. Accessible structural information about its ligand-binding domain renders FXR an attractive target for in silico approaches. Integrated to natural product research these computational tools assist to find novel bioactive compounds showing beneficial effects in prevention and treatment of, for example, the metabolic syndrome, dyslipidemia, atherosclerosis, and type 2 diabetes. Virtual screening experiments of our in-house Chinese Herbal Medicine database with structure-based pharmacophore models, previously generated and validated, revealed mainly lanostane-type triterpenes of the TCM fungus Ganoderma lucidum Karst. as putative FXR ligands. To verify the prediction of the in silico approach, two Ganoderma fruit body extracts and compounds isolated thereof were pharmacologically investigated. Pronounced FXR-inducing effects were observed for the extracts at a concentration of 100 μg/mL. Intriguingly, five lanostanes out of 25 secondary metabolites from G. lucidum, that is, ergosterol peroxide (2), lucidumol A (11), ganoderic acid TR (12), ganodermanontriol (13), and ganoderiol F (14), dose-dependently induced FXR in the low micromolar range in a reporter gene assay. To rationalize the binding interactions, additional pharmacophore profiling and molecular docking studies were performed, which allowed establishing a first structure-activity relationship of the investigated triterpenes.

Farnesoid X receptor modulators: a patent review

IMPORTANCE OF THE FIELD

The farnesoid X receptor (FXR) is a key regulator of cholesterol homeostasis, triglyceride synthesis and lipogenesis. Given some patients' inability to tolerate existing medications, such as the statins, for cholesterol reduction, there is a pressing need for additional medicines to treat dyslipidemia. FXR agonists have emerged in discovery and preclinical efforts to show great potential for the treatment of these and other life-impairing and life-threatening conditions.

AREAS COVERED IN THIS REVIEW

Recent advances in the search for novel FXR modulators are reviewed, with a particular focus on patent applications and peer-reviewed publications disclosed in the past 5 years.

WHAT THE READER WILL GAIN

A total of 72 patent applications and peer-reviewed articles, containing 16 generic structure classes with nearly 5000 novel synthesized structures are reviewed. Where possible, the structure-activity relationship of these structure classes is surveyed, new insights into in vitro and in vivo efficacy of FXR agonists are disclosed, and potential new and promising therapeutic applications are revealed.

TAKE HOME MESSAGE

FXR agonists have proven their efficacy and safety in primates and have significant potential as therapeutic agents for the treatment of dyslipidemia and potentially an array of other disease areas.

Synthesis and pharmacological validation of a novel series of non-steroidal FXR agonists

To overcome the known liabilities of GW4064 a series of analogs were synthesized where the stilbene double bond is replaced by an oxymethylene or amino-methylene linker connecting a terminal benzoic acid with a substituted heteroaryl in the middle ring position. As a result we discovered compounds with increased potency in vitro that cause dose-dependent reduction of plasma triglycerides and cholesterol in db/db mice down to 2 x 1 mg/kg/day upon oral administration.

Antidiabetic sulfonylureas modulate farnesoid X receptor activation and target gene transcription

Background: The sulfonylureas glibenclamide and glimepiride are oral antidiabetic drugs that stimulate insulin secretion by closing pancreatic ATP-dependent potassium channels. The farnesoid X receptor (FXR) is a ligand-activated transcription factor that regulates the expression of several target genes involved in bile acid metabolism and lipid and glucose homeostasis. Methods: In this study we investigated the potential effects of sulfonylureas on the signaling of FXR using a reporter-gene assay, real-time qPCR and computational methods such as molecular docking and molecular dynamic simulations. Results: We demonstrate that glibenclamide and glimepiride modulate FXR activation in a reporter-gene assay and induce FXR target genes in HepG2 cells. Within the docking experiments and molecular dynamics simulation, we found glibenclamide interacting with the ligand-binding domain of FXR and with helix 12. Conclusion: Glibenclamide and glimepiride are potential ligands of FXR and modulate activation and signaling.