Identification of novel liver X receptor activators by structure-based modeling

Structure-based virtual screening for identification of potential non-steroidal LXR modulators against neurodegenerative conditions

Liver X Receptors (LXRs) are members of the nuclear receptor superfamily that regulate cholesterol metabolism. LXRs have been suggested as promising targets against many neurodegenerative diseases (NDDs). The present study was aimed to identify novel non-steroidal molecules that may potentially modulate LXR activity. The structure-based virtual screening (SBVS) was used to search for suitable compounds from the Asinex library. The top hits were selected and filtered based on their binding affinity for LXR α and β isoforms. Based on molecular docking and scoring results, 24 compounds were selected that had binding energy in the range of - 13.9 to - 12 for LXRα and - 12.5 to - 11 for LXRβ, which were higher than the reference ligands (GW3965 and TO901317). Further, the five hits referred to as model 29, 64, 202, 250, 313 were selected by virtue of their binding interactions with amino acid residues at the active site of LXRs. The selected hits were then subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis and blood-brain permeability prediction. It was observed that the selected hits had better pharmacokinetic properties with no toxicity and could cross blood-brain barrier. Further, the selected hits were analysed for dynamic evolution of the system with LXRs by molecular dynamics (MD) simulation at 100 ns using GROMACS. The MD simulation results validated that selected hits possess a remarkable amount of flexibility, stability, compactness, binding energy and exhibited limited conformational modification. The root mean square deviation (RMSD) values of the top-scoring hits complexed with LXRα and LXRβ were 0.05-0.6 nm and 0.05-0.45 nm respectively, which is greater than the protein itself. Altogether the study identified potential non-steroidal LXR modulators that appear to be effective against various neurodegenerative conditions involving perturbed cholesterol and lipid homeostasis.

Review of in silico studies dedicated to the nuclear receptor family: Therapeutic prospects and toxicological concerns

Being in the center of both therapeutic and toxicological concerns, NRs are widely studied for drug discovery application but also to unravel the potential toxicity of environmental compounds such as pesticides, cosmetics or additives. High throughput screening campaigns (HTS) are largely used to detect compounds able to interact with this protein family for both therapeutic and toxicological purposes. These methods lead to a large amount of data requiring the use of computational approaches for a robust and correct analysis and interpretation. The output data can be used to build predictive models to forecast the behavior of new chemicals based on their in vitro activities. This atrticle is a review of the studies published in the last decade and dedicated to NR ligands in silico prediction for both therapeutic and toxicological purposes. Over 100 articles concerning 14 NR subfamilies were carefully read and analyzed in order to retrieve the most commonly used computational methods to develop predictive models, to retrieve the databases deployed in the model building process and to pinpoint some of the limitations they faced.

Screening for liver X receptor modulators: Where are we and for what use?

Liver X receptors (LXRs) are members of the nuclear receptor superfamily that are canonically activated by oxidized derivatives of cholesterol. Since the mid-90s, numerous groups have identified LXRs as endocrine receptors that are involved in the regulation of various physiological functions. As a result, when their expression is genetically modified in mice, phenotypic analyses reveal endocrine disorders ranging from infertility to diabetes and obesity, nervous system pathologies such Alzheimer's or Parkinson's disease, immunological disturbances, inflammatory response, and enhancement of tumour development. Based on such findings, it appears that LXRs could constitute good pharmacological targets to prevent and/or to treat these diseases. This review discusses the various aspects of LXR drug discovery, from the tools available for the screening of potential LXR modulators to the current situational analysis of the drugs in development. LINKED ARTICLES: This article is part of a themed issue on Oxysterols, Lifelong Health and Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.16/issuetoc.

Identfication of Potent LXRβ-Selective Agonists without LXRα Activation by In Silico Approaches

Activating Liver X receptors (LXRs) represents a promising therapeutic option for dyslipidemia. However, activating LXRα may cause undesired lipogenic effects. Discovery of highly LXRβ-selective agonists without LXRα activation were indispensable for dyslipidemia. In this study, in silico approaches were applied to develop highly potent LXRβ-selective agonists based on a series of newly reported 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione-based LXRα/β dual agonists. Initially, Kohonen and stepwise multiple linear regression SW-MLR were performed to construct models for LXRβ agonists and LXRα agonists based on the structural characteristics of LXRα/β dual agonists, respectively. The obtained LXRβ agonist model gave a good predictive ability (R²_train = 0.837, R²_test = 0.843, Q²_LOO = 0.715), and the LXRα agonist model produced even better predictive ability (R²_train = 0.968, R²_test = 0.914, Q²_LOO = 0.895). Also, the two QSAR models were independent and can well distinguish LXRβ and LXRα activity. Then, compounds in the ZINC database met the lower limit of structural similarity of 0.7, compared to the 3-(4-(2-propylphenoxy)butyl)imidazolidine-2,4-dione scaffold subjected to our QSAR models, which resulted in the discovery of ZINC55084484 with an LXRβ prediction value of pEC₅₀ equal to 7.343 and LXRα prediction value of pEC₅₀ equal to −1.901. Consequently, nine newly designed compounds were proposed as highly LXRβ-selective agonists based on ZINC55084484 and molecular docking, of which LXRβ prediction values almost exceeded 8 and LXRα prediction values were below 0.

A de novo substructure generation algorithm for identifying the privileged chemical fragments of liver X receptorβ agonists

Liver X receptorβ (LXRβ) is a promising therapeutic target for lipid disorders, atherosclerosis, chronic inflammation, autoimmunity, cancer and neurodegenerative diseases. Druggable LXRβ agonists have been explored over the past decades. However, the pocket of LXRβ ligand-binding domain (LBD) is too large to predict LXRβ agonists with novel scaffolds based on either receptor or agonist structures. In this paper, we report a de novo algorithm which drives privileged LXRβ agonist fragments by starting with individual chemical bonds (de novo) from every molecule in a LXRβ agonist library, growing the bonds into substructures based on the agonist structures with isomorphic and homomorphic restrictions, and electing the privileged fragments from the substructures with a popularity threshold and background chemical and biological knowledge. Using these privileged fragments as queries, we were able to figure out the rules to reconstruct LXRβ agonist molecules from the fragments. The privileged fragments were validated by building regularized logistic regression (RLR) and supporting vector machine (SVM) models as descriptors to predict a LXRβ agonist activities.

Discriminating agonist and antagonist ligands of the nuclear receptors using 3D-pharmacophores

Nuclear receptors (NRs) constitute an important class of therapeutic targets. We evaluated the performance of 3D structure-based and ligand-based pharmacophore models in predicting the pharmacological profile of NRs ligands using the NRLiSt BDB database. We could generate selective pharmacophores for agonist and antagonist ligands and we found that the best performances were obtained by combining the structure-based and the ligand-based approaches. The combination of pharmacophores that were generated allowed to cover most of the chemical space of the NRLiSt BDB datasets. By screening the whole NRLiSt BDB on our 3D pharmacophores, we demonstrated their selectivity towards their dedicated NRs ligands. The 3D pharmacophores herein presented can thus be used as a predictor of the pharmacological activity of NRs ligands.Graphical AbstractUsing a combination of structure-based and ligand-based pharmacophores, agonist and antagonist ligands of the Nuclear Receptors included in the NRLiSt BDB database could be separated.

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.

Predicting selective liver X receptor β agonists using multiple machine learning methods

The classification models for predicting selective LXRβ agonists were firstly established using multiple machine learning methods. The top models can predict selective LXRβ agonists with chemical structure diversity.

Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening

Agonists of liver X receptors (LXR) α and β are important regulators of cholesterol metabolism, but agonism of the LXRα subtype appears to cause hepatic lipogenesis, suggesting LXRβ-selective activators are attractive new lipid lowering drugs. In this work, pharmacophore modeling and shape-based virtual screening were combined to predict new LXRβ-selective ligands. Out of the 10 predicted compounds, three displayed significant LXR activity. Two activated both LXR subtypes. The third compound activated LXRβ 1.8-fold over LXRα.

Fatty acids regulation of inflammatory and metabolic genes

PURPOSE OF REVIEW

Fatty acids influence human health and diseases in various ways. In recent years, much work has been carried out to elucidate the mechanisms by which dietary fatty acids control short-term and long-term cellular functions. We have reviewed herein the most recent studies on modulation of gene expression by fatty acids. A number of genes respond to transcription factors and present a transcription factor response element in their promoter regions. Fatty acids may exert their effects on metabolism by regulating gene transcription via transcription factors. Understanding how fatty acids control expression of metabolic genes is a promising strategy to be investigated by aiming to treat metabolic diseases such as insulin resistance, obesity, and type 2 diabetes mellitus.

RECENT FINDINGS

Fatty acids exert many of their biological effects through the modulation of the activity of transcription factors, such as sterol regulatory element-binding proteins, peroxisome proliferator-activated receptors, and liver X receptors.

SUMMARY

Fatty acid action through transcription factors controls the expression of several inflammatory and metabolic genes.