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

PharmaCore: The Automatic Generation of 3D Structure-Based Pharmacophore Models from Protein/Ligand Complexes

In this work, we present PharmaCore: a new, completely automatic workflow aimed at generating three-dimensional (3D) structure-based pharmacophore models toward any target of interest. The proposed approach relies on using cocrystallized ligands to create the input files for generating the pharmacophore hypotheses, integrating not only the three-dimensional structural information on the ligand but also data concerning the binding mode of these molecules put in the protein cavity. We developed a Python library that, starting from the specific UniProt ID of the protein under investigation as the only element that requires user intervention, subsequently collects and aligns the corresponding structures bearing a known ligand in a fully automated fashion, bringing them all into the same coordinate system. The protocol includes a final phase in which the aligned small molecules are used to produce the pharmacophore hypotheses directly onto the protein structure using a specific software, e.g., Phase (Schrödinger LLC). To validate the entire procedure and highlight the possible applications in the field of drug discovery and repositioning, we first generated pharmacophores for soluble epoxide hydrolase (sEH) and compared with already-published ones. Then, we reproduced the binding profile of a reported selective binder of ATAD2 bromodomain (AM879), testing it against a panel of 1741 pharmacophores related to 16 epigenetic proteins and automatically generated with PharmaCore, finally disclosing putative unprecedented off-targets. The computational predictions were successfully validated with AlphaScreen assays, highlighting the applicability of the proposed workflow in drug discovery and repositioning. Finally, the process was also validated on tankyrase 2 and SARS-CoV-2 MPro, confirming the robustness of PharmaCore.

Ni-catalyzed enantioconvergent deoxygenative reductive cross-coupling of unactivated alkyl alcohols and aryl bromides

Transition metal-catalyzed enantioconvergent cross-coupling of an alkyl precursor presents a promising method for producing enantioenriched C(sp3) molecules. Because alkyl alcohol is a ubiquitous and abundant family of feedstock in nature, the direct reductive coupling of alkyl alcohol and aryl halide enables efficient access to valuable compounds. Although several strategies have been developed to overcome the high bond dissociation energy of the C - O bond, the asymmetric pattern remains unknown. In this report, we describe the realization of an enantioconvergent deoxygenative reductive cross-coupling of unactivated alkyl alcohol (β-hydroxy ketone) and aryl bromide in the presence of an NHC activating agent. The approach can accommodate substituents of various sizes and functional groups, and its synthetic potency is demonstrated through a gram scale reaction and derivatizations into other compound families. Finally, we apply our convergent method to the efficient asymmetric synthesis of four β-aryl ketones that are natural products or bioactive compounds.

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.

Cheminformatics in Natural Product-based Drug Discovery

This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product-based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product-likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products-inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule-based, similarity-based, shape-based, pharmacophore-based and network-based approaches, docking and machine learning methods.

Characterization of a Structural Leoligin Analog as Farnesoid X Receptor Agonist and Modulator of Cholesterol Transport

The ligand-activated farnesoid X receptor is an emerging therapeutic target for the development of drugs against metabolic syndrome-related diseases. In this context, selective bile acid receptor modulators represent a novel concept for drug development. Selective bile acid receptor modulators act in a target gene- or tissue-specific way and are therefore considered less likely to elicit unwanted side effects. Based on leoligin, a lignan-type secondary plant metabolite from the alpine plant Leontopodium nivale ssp. alpinum, 168 synthesized structural analogs were screened in a farnesoid X receptor in silico pharmacophore-model. Fifty-six virtual hits were generated. These hits were tested in a cell-based farnesoid X receptor transactivation assay and yielded 7 farnesoid X receptor-activating compounds. The most active one being LT-141A, with an EC₅₀ of 6 µM and an E_max of 4.1-fold. This analog did not activate the G protein-coupled bile acid receptor, TGR5, and the metabolic nuclear receptors retinoid X receptor α, liver X receptors α/β, and peroxisome proliferator-activated receptors β/γ. Investigation of different farnesoid X receptor target genes characterized LT-141A as selective bile acid receptor modulators. Functional studies revealed that LT-141A increased cholesterol efflux from THP-1-derived macrophages via enhanced ATP-binding cassette transporter 1 expression. Moreover, cholesterol uptake in differentiated Caco-2 cells was significantly decreased upon LT-141A treatment. In conclusion, the leoligin analog LT-141A selectively activates the nuclear receptor farnesoid X receptor and has an influence on cholesterol transport in 2 model systems.

Refined pharmacophore features for virtual screening of human thromboxane A2 receptor antagonists

For a long time, the structural basis of TXA2 receptor is limited due to the lack of crystal structure information, till the release of the crystal structure of TXA2 receptor, which deepens our understanding about ligand recognition and selectivity mechanisms of this physiologically important receptor. In this research, we report the successful implementation in the discovery of an optimal pharmacophore model of human TXA2 receptor antagonists through virtual screening. Structure-based pharmacophore models were generated based on two crystal structures of human TXA2 receptor (PDB entry 6IIU and 6IIV). Docking simulation revealed interaction modes of the virtual screening hits against TXA2 receptor, which was validated through molecular dynamics simulation and binding free energy calculation. ADMET properties were also analyzed to evaluate the toxicity and physio-chemical characteristics of the hits. The research would provide valuable insight into the binding mechanisms of TXA2 receptor antagonists and thus be helpful for designing novel antagonists.

Cheminformatics in the Identification of Drug Classes for the Treatment of Type 2 Diabetes

Computer-Aided Drug Design has developed into a powerful suite of methods that complement experimental approaches to the identification of new pharmacologically active compounds. In particular, virtual screening has become a standard tool for lead identification. Diverse examples of the application of virtual screening applied to T2DM target proteins have been reported. While several of these indicate successful identification of new lead compounds from synthetic chemical and natural product databases, many of them have been performed on a small scale and with limited validation. Careful study design and collaboration with cheminformaticians and computational chemists will enable these approaches to fulfil their potential for T2DM.

Exploring Novel N-Myristoyltransferase Inhibitors: A Molecular Dynamics Simulation Approach

N-Myristoyltransferase (NMT) is a cytosolic monomeric enzyme involved in the allocation of the myristoyl group to the aminoterminal of glycine in several viral and eukaryotic cellular proteins. NMT has been validated as a potential drug target against kinetoplastid for parasitic protozoa. A multistep virtual screening protocol based on the pharmacophore modeling, molecular docking, and molecular dynamics simulation was carried out. Initially, Maybridge database was virtually screened via a validated pharmacophore model. The effective pharmacophore models were accompanied with exclusion volumes to improve their receiver operating characteristic curve to identify potential NMT inhibitors. The hits identified as actives based on the 3D-pharmacophore model were evaluated by molecular docking studies. In stepwise screening, six compounds were shortlisted for the dynamic simulation to get insights into their binding mode. In conclusion, this study provides fundamental information about the architecture of the binding site and some crucial residues that may provide insights into the development of new antiparasitic agents.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Discovery of new FXR agonists based on 6-ECDCA binding properties by virtual screening and molecular docking

FXR is a member of the nuclear receptor superfamily, which regulates the expression of various genes involved in bile acid, lipid and glucose metabolism. Targeting FXR with small molecules has been exploited to treat lipid-related disorders and diseases such as cholestasis, gallstones and hepatic disorders. In this work, we expand the existing pool of known FXR agonists using a fast hit-to-lead structure-based pharmacophore and docking screening protocol. A set of 25 molecules was selected after screening a large database of commercial chemicals, and experimental tests were carried out to demonstrate their ability to activate FXR. Three novel FXR agonists are reported, namely, one full agonist, more efficient than the endogenous ligand chenodeoxycholic acid, and two partial agonists.

Opportunities and Challenges for Fatty Acid Mimetics in Drug Discovery

Fatty acids beyond their role as an endogenous energy source and storage are increasingly considered as signaling molecules regulating various physiological effects in metabolism and inflammation. Accordingly, the molecular targets involved in formation and physiological activities of fatty acids hold significant therapeutic potential. A number of these fatty acid targets are addressed by some of the oldest and most widely used drugs such as cyclooxygenase inhibiting NSAIDs, whereas others remain unexploited. Compounds orthosterically binding to proteins that endogenously bind fatty acids are considered as fatty acid mimetics. On the basis of their structural resemblance, fatty acid mimetics constitute a family of bioactive compounds showing specific binding thermodynamics and following similar pharmacokinetic mechanisms. This perspective systematically evaluates targets for fatty acid mimetics, investigates their common structural characteristics, and highlights demands in their discovery and design. In summary, fatty acid mimetics share particularly favorable characteristics justifying the conclusion that their therapeutic potential vastly outweighs the challenges in their design.

Identification of G-quadruplex DNA/RNA binders: Structure-based virtual screening and biophysical characterization

BACKGROUND

Recent findings demonstrated that, in mammalian cells, telomere DNA (Tel) is transcribed into telomeric repeat-containing RNA (TERRA), which is involved in fundamental biological processes, thus representing a promising anticancer target. For this reason, the discovery of dual (as well as selective) Tel/TERRA G-quadruplex (G4) binders could represent an innovative strategy to enhance telomerase inhibition.

METHODS

Initially, docking simulations of known Tel and TERRA active ligands were performed on the 3D coordinates of bimolecular G4 Tel DNA (Tel₂) and TERRA (TERRA₂). Structure-based pharmacophore models were generated on the best complexes and employed for the virtual screening of ~257,000 natural compounds. The 20 best candidates were submitted to biophysical assays, which included circular dichroism and mass spectrometry at different K⁺ concentrations.

RESULTS

Three hits were here identified and characterized by biophysical assays. Compound 7 acts as dual Tel₂/TERRA₂ G4-ligand at physiological KCl concentration, while hits 15 and 17 show preferential thermal stabilization for Tel₂ DNA. The different molecular recognition against the two targets was also discussed.

CONCLUSIONS

Our successful results pave the way to further lead optimization to achieve both increased selectivity and stabilizing effect against TERRA and Tel DNA G4s.

GENERAL SIGNIFICANCE

The current study combines for the first time molecular modelling and biophysical assays applied to bimolecular DNA and RNA G4s, leading to the identification of innovative ligand chemical scaffolds with a promising anticancer profile. This article is part of a Special Issue entitled "G-quadruplex" Guest Editor: Dr. Concetta Giancola and Dr. Daniela Montesarchio.

Hit Identification of a Novel Dual Binder for h-telo/c-myc G-Quadruplex by a Combination of Pharmacophore Structure-Based Virtual Screening and Docking Refinement

It is well known that G-quadruplexes are targets of great interest for their roles in crucial biological processes, such as aging and cancer. Hence, a promising strategy for anticancer drug therapy is the stabilization of these structures by small molecules. We report a high-throughput in silico screening of commercial libraries from several different vendors by means of a combined structure-based pharmacophore model approach followed by docking simulations. The compounds selected by the virtual screening procedure were then tested for their ability to interact with human telomeric G-quadruplex folding by circular dichroism, fluorescence spectroscopy, and fluorescence intercalator displacement. Our approach resulted in the identification of a 13-[(dimethylamino)methyl]-12-hydroxy-8H-benzo[c]indolo[3,2,1-ij][1,5]naphthyridin-8-one derivative as a novel promising stabilizer of G-quadruplex structures within the human telomeric and the c-myc promoter sequences.

Discovery of novel, non-acidic mPGES-1 inhibitors by virtual screening with a multistep protocol

Microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors are considered as potential therapeutic agents for the treatment of inflammatory pain and certain types of cancer. So far, several series of acidic as well as non-acidic inhibitors of mPGES-1 have been discovered. Acidic inhibitors, however, may have issues, such as loss of potency in human whole blood and in vivo, stressing the importance of the design and identification of novel, non-acidic chemical scaffolds of mPGES-1 inhibitors. Using a multistep virtual screening protocol, the Vitas-M compound library (∼1.3 million entries) was filtered and 16 predicted compounds were experimentally evaluated in a biological assay in vitro. This approach yielded two molecules active in the low micromolar range (IC50 values: 4.5 and 3.8 μM, respectively).

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.

Identification of potential dual agonists of FXR and TGR5 using e-pharmacophore based virtual screening

Farnesoid X receptor and Takeda G-protein-coupled receptor-5 are well known bile acid receptors and act as promising targets for the drug development and treatment of diabetes.

Identifying farnesoid X receptor agonists by naïve Bayesian and recursive partitioning approaches

For the first time, NB and RP were successfully employed to predict FXR agonists. Two new FXR agonists were identified with the models, and confirmed with cell-based experiments.

An updated review on drug-induced cholestasis: mechanisms and investigation of physicochemical properties and pharmacokinetic parameters

Drug-induced cholestasis is an important form of acquired liver disease and is associated with significant morbidity and mortality. Bile acids are key signaling molecules, but they can exert toxic responses when they accumulate in hepatocytes. This review focuses on the physiological mechanisms of drug-induced cholestasis associated with altered bile acid homeostasis due to direct (e.g., bile acid transporter inhibition) or indirect (e.g., activation of nuclear receptors, altered function/expression of bile acid transporters) processes. Mechanistic information about the effects of a drug on bile acid homeostasis is important when evaluating the cholestatic potential of a compound, but experimental data often are not available. The relationship between physicochemical properties, pharmacokinetic parameters, and inhibition of the bile salt export pump among 77 cholestatic drugs with different pathophysiological mechanisms of cholestasis (i.e., impaired formation of bile vs. physical obstruction of bile flow) was investigated. The utility of in silico models to obtain mechanistic information about the impact of compounds on bile acid homeostasis to aid in predicting the cholestatic potential of drugs is highlighted.

A combination of receptor-based pharmacophore modeling & QM techniques for identification of human chymase inhibitors

Inhibition of chymase is likely to divulge therapeutic ways for the treatment of cardiovascular diseases, and fibrotic disorders. To find novel and potent chymase inhibitors and to provide a new idea for drug design, we used both ligand-based and structure-based methods to perform the virtual screening(VS) of commercially available databases. Different pharmacophore models generated from various crystal structures of enzyme may depict diverse inhibitor binding modes. Therefore, multiple pharmacophore-based approach is applied in this study. X-ray crystallographic data of chymase in complex with different inhibitors were used to generate four structure-based pharmacophore models. One ligand-based pharmacophore model was also developed from experimentally known inhibitors. After successful validation, all pharmacophore models were employed in database screening to retrieve hits with novel chemical scaffolds. Drug-like hit compounds were subjected to molecular docking using GOLD and AutoDock. Finally four structurally diverse compounds with high GOLD score and binding affinity for several crystal structures of chymase were selected as final hits. Identification of final hits by three different pharmacophore models necessitates the use of multiple pharmacophore-based approach in VS process. Quantum mechanical calculation is also conducted for analysis of electrostatic characteristics of compounds which illustrates their significant role in driving the inhibitor to adopt a suitable bioactive conformation oriented in the active site of enzyme. In general, this study is used as example to illustrate how multiple pharmacophore approach can be useful in identifying structurally diverse hits which may bind to all possible bioactive conformations available in the active site of enzyme. The strategy used in the current study could be appropriate to design drugs for other enzymes as well.

Virtual screening as a strategy for the identification of xenobiotics disrupting corticosteroid action

BACKGROUND

Impaired corticosteroid action caused by genetic and environmental influence, including exposure to hazardous xenobiotics, contributes to the development and progression of metabolic diseases, cardiovascular complications and immune disorders. Novel strategies are thus needed for identifying xenobiotics that interfere with corticosteroid homeostasis. 11β-hydroxysteroid dehydrogenase 2 (11β-HSD2) and mineralocorticoid receptors (MR) are major regulators of corticosteroid action. 11β-HSD2 converts the active glucocorticoid cortisol to the inactive cortisone and protects MR from activation by glucocorticoids. 11β-HSD2 has also an essential role in the placenta to protect the fetus from high maternal cortisol concentrations.

METHODS AND PRINCIPAL FINDINGS

We employed a previously constructed 3D-structural library of chemicals with proven and suspected endocrine disrupting effects for virtual screening using a chemical feature-based 11β-HSD pharmacophore. We tested several in silico predicted chemicals in a 11β-HSD2 bioassay. The identified antibiotic lasalocid and the silane-coupling agent AB110873 were found to concentration-dependently inhibit 11β-HSD2. Moreover, the silane AB110873 was shown to activate MR and stimulate mitochondrial ROS generation and the production of the proinflammatory cytokine interleukin-6 (IL-6). Finally, we constructed a MR pharmacophore, which successfully identified the silane AB110873.

CONCLUSIONS

Screening of virtual chemical structure libraries can facilitate the identification of xenobiotics inhibiting 11β-HSD2 and/or activating MR. Lasalocid and AB110873 belong to new classes of 11β-HSD2 inhibitors. The silane AB110873 represents to the best of our knowledge the first industrial chemical shown to activate MR. Furthermore, the MR pharmacophore can now be used for future screening purposes.

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

Liver X receptors (LXRs) are members of the nuclear receptor family. Activators of LXRs are of high pharmacological interest as LXRs regulate cholesterol, fatty acid, and carbohydrate metabolism as well as inflammatory processes. On the basis of different X-ray crystal structures, we established a virtual screening workflow for the identification of novel LXR modulators. A two-step screening concept to identify active compounds included 3D-pharmacophore filters and rescoring by shape alignment. Eighteen virtual hits were tested in vitro applying a reporter gene assay, where concentration-dependent activity was proven for four novel lead structures. The most active compound 10, a 1,4-naphthochinone, has an estimated EC₅₀ of around 5 μM.

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.

Investigation of imatinib and other approved drugs as starting points for antidiabetic drug discovery with FXR modulating activity

A self-organizing map (SOM) is a virtual screening method used for correlation of molecular structure and potential biological activity on a certain target and offers a way to represent multi-dimensional data of large databases in a two-dimensional space. Large databases, for example the DrugBank database, provide information about biological activity and chemical structure of small molecules and are widely used in drug development for identification of new lead structures. The farnesoid X receptor (FXR) is a ligand activated transcription factor involved in key regulation mechanisms within glucose and lipid homeostasis. Although FXR became an established target in drug development for diseases associated with lipid, glucose or hepatic disorders during the last decade, none of the developed compounds have reached later phases of clinical development so far. We used a SOM trained with known FXR ligands to screen the DrugBank database for potential ligands for FXR. In this article, we report the successful identification of six approved drugs out of the Drugbank as FXR modulators (ketoconazole, pentamidine, dobutamine, imatinib, papaverine and montelukast) by using a SOM for screening of the DrugBank database. We show FXR modulation by selected compounds in a full length FXR transactivation assay and modulation of a FXR target gene by imatinib.

In silico virtual screening approaches for anti-viral drug discovery

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.: