Discovery of Novel CB2 Receptor Ligands by a Pharmacophore-Based Virtual Screening Workflow

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.

Identification of Novel CB2 Ligands through Virtual Screening and In Vitro Evaluation

Cannabinoid receptor type 2 (CB2) is a very promising therapeutic target for a variety of potential indications. However, despite the existence of multiple high affinity CB2 ligands, none have yet been approved as a drug. Therefore, it would be beneficial to explore new chemotypes of CB2 ligands. The recent elucidation of CB2 tertiary structure allows for rational hit identification with structure-based (SB) methods. In this study, we established a virtual screening workflow based on SB techniques augmented with ligand-based ones, including molecular docking, MM-GBSA binding energy calculations, pharmacophore screening, and QSAR. We screened nearly 7 million drug-like, commercially available compounds. We selected 16 molecules for in vitro evaluation and identified two novel, selective CB2 antagonists with K_i values of 65 and 210 nM. Both compounds are structurally diverse from CB2 ligands known to date. The established virtual screening protocol may prove useful for hit identification for CB2 and similar molecular targets. The two novel CB2 ligands provide a desired starting point for future optimization and development of potential drugs.

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

Identification of Novel Antagonists Targeting Cannabinoid Receptor 2 Using a Multi-Step Virtual Screening Strategy

The endocannabinoid system plays an essential role in the regulation of analgesia and human immunity, and Cannabinoid Receptor 2 (CB2) has been proved to be an ideal target for the treatment of liver diseases and some cancers. In this study, we identified CB2 antagonists using a three-step “deep learning–pharmacophore–molecular docking” virtual screening approach. From the ChemDiv database (1,178,506 compounds), 15 hits were selected and tested by radioligand binding assays and cAMP functional assays. A total of 7 out of the 15 hits were found to exhibit binding affinities in the radioligand binding assays against CB2 receptor, with a pK_i of 5.15–6.66, among which five compounds showed antagonistic activities with pIC₅₀ of 5.25–6.93 in the cAMP functional assays. Among these hits, Compound 8 with the 4H-pyrido[1,2-a]pyrimidin-4-one scaffold showed the best binding affinity and antagonistic activity with a pK_i of 6.66 and pIC₅₀ of 6.93, respectively. The new scaffold could serve as a lead for further development of CB2 drugs. Additionally, we hope that the model in this study could be further utilized to identify more novel CB2 receptor antagonists, and the developed approach could also be used to design potent ligands for other therapeutic targets.

Ligand based 3D-QSAR model, pharmacophore, molecular docking and ADME to identify potential fibroblast growth factor receptor 1 inhibitors

The FGF/FGFR system may affect tumor cells and stromal microenvironment through autocrine and paracrine stimulation, thereby significantly promoting oncogene transformation and tumor growth. Abnormal expression of FGFR1 in cells is considered to be the main cause of tumorigenesis and a potential target for the treatment of cancer. In this study, a combination of structure-based drug carriers and molecular docking-based virtual screening was used to screen new potential FGFR1 inhibitors. Forty eight known inhibitors were collected to establish 3 D-QSAR models and pharmacophore models, investigate the relationship between the activity and conformation of compounds, and verify the efficiency of pharmacophore. In Accelrys Discovery Studio 2016, the ZINC database was filtered by Lipinski's Rule of Five and SMART's filtration. Then, Hypo01 was used for virtual screening of ZINC database. Compounds with predicted activity values less than 1 μM were molecularly docked with FGFR1 protein crystals, the docking results were observed, and the interaction between compounds and targets was studied. The absorption, distribution, metabolism and excretion (ADME) and toxicity of potential inhibitors were studied, and a compound with new structural scaffolds were obtained. It could be further studied to explore their better therapeutic effects.Communicated by Ramaswamy H. Sarma.

Target identification, screening and in vivo evaluation of pyrrolone-fused benzosuberene compounds against human epilepsy using Zebrafish model of pentylenetetrazol-induced seizures

Pyrrolone-fused benzosuberene (PBS) compounds were semi-synthesized from α,β,γ-Himachalenes extracted from the essential oil of Cedrus deodara following amino-vinyl-bromide substituted benzosuberenes as intermediates. These PBSs compounds classified as an attractive source of therapeutics. The α-isoform of PI3K which is a pivotal modulator of PI3K/AKT/mTOR signaling pathway, responsible for neurological disorders like epilepsy, found as a potential target molecule against these 17 semi-synthesized PBS compounds using in silico ligand-based pharmacophore mapping and target screening. The compounds screened using binding affinities, ADMET properties, and toxicity that were accessed by in silico docking simulations and pharmacokinetics profiling. Ultimately two compounds viz., PBS-8 and PBS-9 were selected for further in vivo evaluation using a zebrafish (Danio rerio) model of pentylenetetrazol (PTZ)-induced clonic convulsions. Additionally, gene expression studies performed for the genes of the PI3K/AKT/mTOR pathway which further validated our results. In conclusion, these findings suggested that PBS-8 is a promising candidate that could bedeveloped as a potential antiepileptic.

A MALDI-TOF-based Method for Studying the Transport of BBB Shuttles-Enhancing Sensitivity and Versatility of Cell-Based In Vitro Transport Models

In recent decades, peptide blood-brain barrier shuttles have emerged as a promising solution for brain drugs that are not able to enter this organ. The research and development of these compounds involve the use of in vitro cell-based models of the BBB. Nevertheless, peptide transport quantification implies the use of large amounts of peptide (upper micromolar range for RP-HPLC-PDA) or of derivatives (e.g. fluorophore or quantum-dot attachment, radiolabeling) in the donor compartment in order to enhance the detection of these molecules in the acceptor well, although their structure is highly modified. Therefore, these methodologies either hamper the use of low peptide concentrations, thus hindering mechanistic studies, or do not allow the use of the unmodified peptide. Here we successfully applied a MALDI-TOF MS methodology for transport quantification in an in vitro BBB cell-based model. A light version of the acetylated peptide was evaluated, and the transport was subsequently quantified using a heavy internal standard (isotopically acetylated). We propose that this MALDI-TOF MS approach could also be applied to study the transport across other biological barriers using the appropriate in vitro transport models (e.g. Caco-2, PAMPA).

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.

Ligand-based virtual screening identifies a family of selective cannabinoid receptor 2 agonists

The cannabinoid receptor 2 (CB2R) has been linked with the regulation of inflammation, and selective receptor activation has been proposed as a target for the treatment of a range of inflammatory diseases such as atherosclerosis and arthritis. In order to identify selective CB2R agonists with appropriate physicochemical and ADME properties for future evaluation in vivo, we first performed a ligand-based virtual screen. Subsequent medicinal chemistry optimisation studies led to the identification of a new class of selective CB2R agonists. Several examples showed high levels of activity (EC50<200 nM) and binding affinity (Ki<200 nM) for the CB2R, and no detectable activity at the CB1R. The most promising example, DIAS2, also showed favourable in vitro metabolic stability and absorption properties along with a clean selectivity profile when evaluated against a panel of GPCRs and kinases.

An efficient multistep ligand-based virtual screening approach for GPR40 agonists

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein-ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.

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.

Software and resources for computational medicinal chemistry

Computer-aided drug design plays a vital role in drug discovery and development and has become an indispensable tool in the pharmaceutical industry. Computational medicinal chemists can take advantage of all kinds of software and resources in the computer-aided drug design field for the purposes of discovering and optimizing biologically active compounds. This article reviews software and other resources related to computer-aided drug design approaches, putting particular emphasis on structure-based drug design, ligand-based drug design, chemical databases and chemoinformatics tools.

Three-dimensional quantitative structure-selectivity relationships analysis guided rational design of a highly selective ligand for the cannabinoid receptor 2

This paper describes a three-dimensional quantitative structure-selectivity relationships (3D-QSSR) study for selectivity of a series of ligands for cannabinoid CB1 and CB2 receptors. 3D-QSSR exploration was expected to provide design information for drugs with high selectivity toward the CB2 receptor. The proposed 3D computational model was performed by Phase and generated taking into account a number of structurally diverse compounds characterized by a wide range of selectivity index values. The model proved to be predictive, with r2 of 0.95 and Q2 of 0.63. In order to get prospective experimental validation, the selectivity of an external data set of 39 compounds reported in the literature was predicted. The correlation coefficient (r2=0.56) obtained on this unrelated test set provided evidence that the correlation shown by the model was not a chance result. Subsequently, we essayed the ability of our approach to help the design of new CB2-selective ligands. Accordingly, based on our interest in studying the cannabinergic properties of quinolones, the N-(adamantan-1-yl)-4-oxo-8-methyl-1-pentyl-1,4-dihydroquinoline-3-carboxamide (65) was considered as a potential synthetic target. The log(SI) value predicted by using our model was indicative of high CB2 selectivity for such a compound, thus spurring us to synthesize it and to evaluate its CB1 and CB2 receptor affinity. Compound 65 was found to be an extremely selective CB2 ligand as it displayed high CB2 affinity (Ki=4.9 nM), while being devoid of CB1 affinity (Ki>10,000 nM). The identification of a new selective CB2 receptor ligand lends support for the practicability of quantitative ligand-based selectivity models for cannabinoid receptors. These drug discovery tools might represent a valuable complementary approach to docking studies performed on homology models of the receptors.

Activation of cannabinoid 2 receptors protects against cerebral ischemia by inhibiting neutrophil recruitment

Activation of the cannabinoid 2 receptor (CB(2)) reduces ischemic injury in several organs. However, the mechanisms underlying this protective action are unclear. In a mouse model of ischemic stroke, we show that the CB(2) agonist JWH-133 (1 mg . kg(-1) . d(-1)) decreases the infarct size measured 3 d after onset of ischemia. The neuroprotective effect of JWH-133 was lost in CB(2)-deficient mice, confirming the specificity of JWH-133. Analysis of bone marrow chimeric mice revealed that bone marrow-derived cells mediate the CB(2) effect on ischemic brain injury. CB(2) activation reduced the number of neutrophils in the ischemic brain as shown by FACS analysis and by measuring the levels of the neutrophil marker enzyme myeloperoxidase. Indeed, we found in vitro that CB(2) activation inhibits adherence of neutrophils to brain endothelial cells. JWH-133 (1 microM) also interfered with the migration of neutrophils induced by the endogenous chemokine CXCL2 (30 ng/ml) through activation of the MAP kinase p38. This effect on neutrophils is likely responsible for the neuroprotection mediated by JWH-133 because JWH-133 was no longer protective when neutrophils were depleted. In conclusion, our data demonstrate that by activating p38 in neutrophils, CB(2) agonists inhibit neutrophil recruitment to the brain and protect against ischemic brain injury.-Murikinati, S., Jüttler, E., Keinert, T., Ridder, D. A., Muhammad, S., Waibler, Z., Ledent, C., Zimmer, A., Kalinke, U., Schwaninger, M. Activation of cannabinoid 2 receptors protects against cerebral ischemia by inhibiting neutrophil recruitment.