Structure-based discovery of A2A adenosine receptor ligands

Hit-to-Lead Optimization of Heterocyclic Carbonyloxycarboximidamides as Selective Antagonists at Human Adenosine A3 Receptor

Antagonism of the human adenosine A3 receptor (hA3R) has potential therapeutic application. Alchemical relative binding free energy calculations of K18 and K32 suggested that the combination of a 3-(2,6-dichlorophenyl)-isoxazolyl group with 2-pyridinyl at the ends of a carbonyloxycarboximidamide group should improve hA3R affinity. Of the 25 new analogues synthesized, 37 and 74 showed improved hA3R affinity compared to K18 (and K32). This was further improved through the addition of a bromine group to the 2-pyridinyl at the 5-position, generating compound 39. Alchemical relative binding free energy calculations, mutagenesis studies and MD simulations supported the compounds' binding pattern while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, so highlighting the importance of rigidification of the carbonyloxycarboximidamide moiety. MD simulations highlighted the importance of rigidification of the carbonyloxycarboximidamide, while suggesting that the bromine of 39 inserts deep into the hA3R orthosteric pocket, which was supported through mutagenesis studies 39 also selectively antagonized endogenously expressed hA3R in nonsmall cell lung carcinoma cells, while pharmacokinetic studies indicated low toxicity enabling in vivo evaluation. We therefore suggest that 39 has potential for further development as a high-affinity hA3R antagonist.

Know your molecule: pharmacological characterization of drug candidates to enhance efficacy and reduce late-stage attrition

Despite advances in chemical, computational and biological sciences, the rate of attrition of drug candidates in clinical development is still high. A key point in the small-molecule discovery process that could provide opportunities to help address this challenge is the pharmacological characterization of hit and lead compounds, culminating in the selection of a drug candidate. Deeper characterization is increasingly important, because the 'quality' of drug efficacy, at least for G protein-coupled receptors (GPCRs), is now understood to be much more than activation of commonly evaluated pathways such as cAMP signalling, with many more 'efficacies' of ligands that could be harnessed therapeutically. Such characterization is being enabled by novel assays to characterize the complex behaviour of GPCRs, such as biased signalling and allosteric modulation, as well as advances in structural biology, such as cryo-electron microscopy. This article discusses key factors in the assessments of the pharmacology of hit and lead compounds in the context of GPCRs as a target class, highlighting opportunities to identify drug candidates with the potential to address limitations of current therapies and to improve the probability of them succeeding in clinical development.

DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening

Recently emerging generative AI models enable us to produce a vast number of compounds for potential applications. While they can provide novel molecular structures, the synthetic feasibility of the generated molecules is often questioned. To address this issue, a few recent studies have attempted to use deep learning models to estimate the synthetic accessibility of many molecules rapidly. However, retrosynthetic analysis tools used to train the models rely on reaction templates automatically extracted from a large reaction database that are not domain-specific and may exhibit low chemical correctness. To overcome this limitation, we introduce DFRscore (Drug-Focused Retrosynthetic score), a deep learning-based approach for a more practical assessment of synthetic accessibility in drug discovery. The DFRscore model is trained exclusively on drug-focused reactions, providing a predicted number of minimally required synthetic steps for each compound. This approach enables practitioners to filter out compounds that do not meet their desired level of synthetic accessibility at an early stage of high-throughput virtual screening for accelerated drug discovery. The proposed strategy can be easily adapted to other domains by adjusting the synthesis planning setup of the reaction templates and starting materials.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Helix encoder: a compound-protein interaction prediction model specifically designed for class A GPCRs

Class A G protein-coupled receptors (GPCRs) represent the largest class of GPCRs. They are essential targets of drug discovery and thus various computational approaches have been applied to predict their ligands. However, there are a large number of orphan receptors in class A GPCRs and it is difficult to use a general protein-specific supervised prediction scheme. Therefore, the compound-protein interaction (CPI) prediction approach has been considered one of the most suitable for class A GPCRs. However, the accuracy of CPI prediction is still insufficient. The current CPI prediction model generally employs the whole protein sequence as the input because it is difficult to identify the important regions in general proteins. In contrast, it is well-known that only a few transmembrane helices of class A GPCRs play a critical role in ligand binding. Therefore, using such domain knowledge, the CPI prediction performance could be improved by developing an encoding method that is specifically designed for this family. In this study, we developed a protein sequence encoder called the Helix encoder, which takes only a protein sequence of transmembrane regions of class A GPCRs as input. The performance evaluation showed that the proposed model achieved a higher prediction accuracy compared to a prediction model using the entire protein sequence. Additionally, our analysis indicated that several extracellular loops are also important for the prediction as mentioned in several biological researches.

Structure-based virtual screening discovers potent and selective adenosine A1 receptor antagonists

Development of subtype-selective leads is essential in drug discovery campaigns targeting G protein-coupled receptors (GPCRs). Herein, a structure-based virtual screening approach to rationally design subtype-selective ligands was applied to the A₁ and A_2A adenosine receptors (A₁R and A_2AR). Crystal structures of these closely related subtypes revealed a non-conserved subpocket in the binding sites that could be exploited to identify A₁R selective ligands. A library of 4.6 million compounds was screened computationally against both receptors using molecular docking and 20 A₁R selective ligands were predicted. Of these, seven antagonized the A₁R with micromolar activities and several compounds displayed slight selectivity for this subtype. Twenty-seven analogs of two discovered scaffolds were designed, resulting in antagonists with nanomolar potency and up to 76-fold A₁R-selectivity. Our results show the potential of structure-based virtual screening to guide discovery and optimization of subtype-selective ligands, which could facilitate the development of safer drugs.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

In-silico identification of novel inhibitors for human Aurora kinase B form the ZINC database using molecular docking-based virtual screening

Introduction: Aurora kinase enzymes play critical functions in mammals. Aurora kinases are mitotic regulators that are involved in a variety of processes during cell division. The overexpression of these enzymes is associated with tumour formation and is symptomatic of clinical circumstances in cancer patients who have been diagnosed.

Materials and methods: The current study reports an in-silico virtual screening (VS) and molecular docking analysis of 2500 compounds retrieved from the ZINC database and five current clinical trial compounds against Aurora Kinase B using AutoDock Vina to identify potential inhibitors.

Results and discussion: The top six compounds that resulted from the screening were ZINC00190959, ZINC07889110, ZINC0088285, ZINC01404326, ZINC00882846 and ZINC08813187, which showed lower free energy of binding (FEB) against the target protein binding pocket. The FEB were as follows: -11.92, -11.85, -11.46, -11.33, -11.21 and -11.1 kcal/mol, using AutoDock, and -11.7, -11.5, -11.2, -11.0, -10.8 and -10.6 kcal/mol for AutoDock Vina, respectively. These findings were superior to those obtained with the co-crystallized ligand VX-680, with a -7.5 kcal/mol and the current clinical trial drug. Finally, using a VS and molecular docking approach, novel Aurora kinase B inhibitors were effectively identified from the ZINC database fulfilling the Lipinski rule of five with low FEB and functional molecular interactions with the target protein.

Conclusion: The findings suggest that the six compounds could be used as a potential agent for cancer treatments.

Graphical abstract

Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations

Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A₁R/A₃R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine (A17) displaying the highest affinity at both receptors with a long residence time of binding, as determined using a NanoBRET-based assay. Two binding orientations of A17 produce stable complexes inside the orthosteric binding area of A₁R in molecular dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity experiments. Interestingly, for drug design purposes, the mutation of L250^6.51 to alanine increased the binding affinity of A17 at A₁R. We explored the structure-activity relationships against A₁R using alchemical binding free energy calculations with the thermodynamic integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calculated and experimental relative binding free energies.

Adenosine A2A receptor antagonists: from caffeine to selective non-xanthines

A long evolution of knowledge of the psychostimulant caffeine led in the 1960s to another purine natural product, adenosine and its A2A receptor. Adenosine is a short-lived autocrine/paracrine mediator that acts pharmacologically at four different adenosine receptors in a manner opposite to the pan-antagonist caffeine and serves as an endogenous allostatic regulator. Although detrimental in the developing brain, caffeine appears to be cerebroprotective in aging. Moderate caffeine consumption in adults, except in pregnancy, may also provide benefit in pain, diabetes, and kidney and liver disorders. Inhibition of A2A receptors is one of caffeine's principal effects and we now understand this interaction at the atomic level. The A2A receptor has become a prototypical example of utilizing high-resolution structures of GPCRs for the rational design of chemically diverse drug molecules. The previous focus on discovery of selective A2A receptor antagonists for neurodegenerative diseases has expanded to include immunotherapy for cancer, and clinical trials have ensued. LINKED ARTICLES: This article is part of a themed issue on Structure Guided Pharmacology of Membrane Proteins (BJP 75th Anniversary). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.14/issuetoc.

A2A Adenosine Receptor Antagonists in Neurodegenerative Diseases

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia worldwide, with approximately 6 million cases reported in America in 2020. The clinical signs of AD include cognitive dysfunction, apathy, anxiety and neuropsychiatric signs, and pathogenetic mechanisms that involve amyloid peptide-β extracellular accumulation and tau hyperphosphorylation. Unfortunately, current drugs to treat AD can provide only symptomatic relief but are not disease-modifying molecules able to revert AD progression. The endogenous modulator adenosine, through A_2A receptor activation, plays a role in synaptic loss and neuroinflammation, which are crucial for cognitive impairment and memory damage.

OBJECTIVE

In this review, recent advances covering A_2A adenosine receptor antagonists will be extensively reviewed, providing a basis for the rational design of future A_2A inhibitors.

METHODS

Herein, the literature on A_2A adenosine receptors and their role in synaptic plasticity and neuroinflammation, as well as the effects of A_2A antagonism in animal models of AD and in humans, are reviewed. Furthermore, current chemical and structure-based strategies are presented.

RESULTS

Caffeine, the most widely consumed natural product stimulant and an A_2A antagonist, improves human memory. Similarly, synthetic A_2A receptor antagonists, as described in this review, may provide a means to fight AD.

CONCLUSION

This review highlights the clinical potential of A_2A adenosine receptor antagonists as a novel approach to treat patients with AD.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Physics of biomolecular recognition and conformational dynamics

Biomolecular recognition usually leads to the formation of binding complexes, often accompanied by large-scale conformational changes. This process is fundamental to biological functions at the molecular and cellular levels. Uncovering the physical mechanisms of biomolecular recognition and quantifying the key biomolecular interactions are vital to understand these functions. The recently developed energy landscape theory has been successful in quantifying recognition processes and revealing the underlying mechanisms. Recent studies have shown that in addition to affinity, specificity is also crucial for biomolecular recognition. The proposed physical concept of intrinsic specificity based on the underlying energy landscape theory provides a practical way to quantify the specificity. Optimization of affinity and specificity can be adopted as a principle to guide the evolution and design of molecular recognition. This approach can also be used in practice for drug discovery using multidimensional screening to identify lead compounds. The energy landscape topography of molecular recognition is important for revealing the underlying flexible binding or binding-folding mechanisms. In this review, we first introduce the energy landscape theory for molecular recognition and then address four critical issues related to biomolecular recognition and conformational dynamics: (1) specificity quantification of molecular recognition; (2) evolution and design in molecular recognition; (3) flexible molecular recognition; (4) chromosome structural dynamics. The results described here and the discussions of the insights gained from the energy landscape topography can provide valuable guidance for further computational and experimental investigations of biomolecular recognition and conformational dynamics.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.

Discovery of novel dual adenosine A1/A2A receptor antagonists using deep learning, pharmacophore modeling and molecular docking

Adenosine receptors (ARs) have been demonstrated to be potential therapeutic targets against Parkinson's disease (PD). In the present study, we describe a multistage virtual screening approach that identifies dual adenosine A1 and A2A receptor antagonists using deep learning, pharmacophore models, and molecular docking methods. Nineteen hits from the ChemDiv library containing 1,178,506 compounds were selected and further tested by in vitro assays (cAMP functional assay and radioligand binding assay); of these hits, two compounds (C8 and C9) with 1,2,4-triazole scaffolds possessing the most potent binding affinity and antagonistic activity for A1/A2A ARs at the nanomolar level (pKi of 7.16-7.49 and pIC50 of 6.31-6.78) were identified. Further molecular dynamics (MD) simulations suggested similarly strong binding interactions of the complexes between the A1/A2A ARs and two compounds (C8 and C9). Notably, the 1,2,4-triazole derivatives (compounds C8 and C9) were identified as the most potent dual A1/A2A AR antagonists in our study and could serve as a basis for further development. The effective multistage screening approach developed in this study can be utilized to identify potent ligands for other drug targets.

C@PA: Computer-Aided Pattern Analysis to Predict Multitarget ABC Transporter Inhibitors

Based on literature reports of the last two decades, a computer-aided pattern analysis (C@PA) was implemented for the discovery of novel multitarget ABCB1 (P-gp), ABCC1 (MRP1), and ABCG2 (BCRP) inhibitors. C@PA included basic scaffold identification, substructure search and statistical distribution, as well as novel scaffold extraction to screen a large virtual compound library. Over 45,000 putative and novel broad-spectrum ABC transporter inhibitors were identified, from which 23 were purchased for biological evaluation. Our investigations revealed five novel lead molecules as triple ABCB1, ABCC1, and ABCG2 inhibitors. C@PA is the very first successful computational approach for the discovery of promiscuous ABC transporter inhibitors.

Repurposing of Omarigliptin as a Neuroprotective Agent Based on Docking with A2A Adenosine and AChE Receptors, Brain GLP-1 Response and Its Brain/Plasma Concentration Ratio after 28 Days Multiple Doses in Rats Using LC-MS/MS

The authors in the current work suggested the potential repurposing of omarigliptin (OMR) for neurodegenerative diseases based on three new findings that support the preliminary finding of crossing BBB after a single dose study in the literature. The first finding is the positive results of the docking study with the crystal structures of A_2A adenosine (A2AAR) and acetylcholine esterase (AChE) receptors. A2AAR is a member of non-dopaminergic GPCR superfamily receptor proteins and has essential role in regulation of glutamate and dopamine release in Parkinson’s disease while AChE plays a major role in Alzheimer’s disease as the primary enzyme responsible for the hydrolytic metabolism of the neurotransmitter acetylcholine into choline and acetate. Docking showed that OMR perfectly fits into A2AAR binding pocket forming a distinctive hydrogen bond with Threonine 256. Besides other non-polar interactions inside the pocket suggesting the future of the marketed anti-diabetic drug (that cross BBB) as a potential antiparkinsonian agent while OMR showed perfect fit inside AChE receptor binding site smoothly because of its optimum length and the two fluorine atoms that enables quite lean fitting. Moreover, a computational comparative study of OMR docking, other 12 DPP-4 inhibitors and 11 SGLT-2 inhibitors was carried out. Secondly, glucagon-like peptide-1 (GLP-1) concentration in rats’ brain tissue was determined by the authors using sandwich GLP-1 ELISA kit bio-analysis to ensure the effect of OMR after the multiple doses’ study. Brain GLP-1 concentration was elevated by 1.9-fold following oral multiple doses of OMR (5 mg/kg/day, p.o. for 28 days) as compared to the control group. The third finding is the enhanced BBB crossing of OMR after 28 days of multiple doses that had been studied using LC-MS/MS method with enhanced liquid–liquid extraction. A modified LC-MS/MS method was established for bioassay of OMR in rats’ plasma (10–3100 ng/mL) and rats’ brain tissue (15–2900 ng/mL) using liquid–liquid extraction. Alogliptin (ALP) was chosen as an internal standard (IS) due to its LogP value of 1.1, which is very close to the LogP of OMR. Extraction of OMR from samples of both rats’ plasma and rats’ brain tissue was effectively achieved with ethyl acetate as the extracting solvent after adding 1N sodium carbonate to enhance the drug migration, while choosing acetonitrile to be the diluent solvent for the IS to effectively decrease any emulsion between the layers in the stated method of extraction. Validation results were all pleasing including good stability studies with bias of value below 20%. Concentration of OMR in rats’ plasma were determined after 2 h of the latest dose from 28 days multiple doses, p.o, 5 mg/kg/day. It was found to be 1295.66 ± 684.63 ng/mL estimated from the bio-analysis regression equation. OMR passed through the BBB following oral administration and exhibited concentration of 543.56 ± 344.15 ng/g in brain tissue, taking in consideration the dilution factor of 10. The brain/plasma concentration ratio of 0.42 (543.56/1295.66) was used to illustrate the penetration power through the BBB after the multiple doses for 28 days. Results showed that OMR passed through the BBB more effectively in the multiple dose study as compared to the previously published single dose study by the authors. Thus, the present study suggests potential repositioning of OMR as antiparkinsonian agent that will be of interest for researchers interested in neurodegenerative diseases.

Identification of a New Heterocyclic Scaffold for Inhibitors of the Polo-Box Domain of Polo-like Kinase 1

As a mitotic-specific target widely deregulated in various human cancers, polo-like kinase 1 (Plk1) has been extensively explored for anticancer activity and drug discovery. Although multiple catalytic domain inhibitors were tested in preclinical and clinical studies, their efficacies are limited by dose-limiting cytotoxicity, mainly from off-target cross reactivity. The C-terminal noncatalytic polo-box domain (PBD) of Plk1 has emerged as an attractive target for generating new protein-protein interaction inhibitors. Here, we identified a 1-thioxo-2,4-dihydro-[1,2,4]triazolo[4,3-a]quinazolin-5(1H)-one scaffold that efficiently inhibits Plk1 PBD but not its related Plk2 and Plk3 PBDs. Structure-activity relationship studies led to multiple inhibitors having ≥10-fold higher inhibitory activity than the previously characterized Plk1 PBD-specific phosphopeptide, PLHSpT (Kd ∼ 450 nM). In addition, S-methyl prodrugs effectively inhibited mitotic progression and cell proliferation and their metabolic stability was determined. These data describe a novel class of small-molecule inhibitors that offer a promising avenue for future drug discovery against Plk1-addicted cancers.

Fragment Binding to the Nsp3 Macrodomain of SARS-CoV-2 Identified Through Crystallographic Screening and Computational Docking

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Purinergic Signaling: Impact of GPCR Structures on Rational Drug Design

The purinergic signaling system includes membrane-bound receptors for extracellular purines and pyrimidines, and enzymes/transporters that regulate receptor activation by endogenous agonists. Receptors include: adenosine (A1 , A2A , A2B, and A3 ) and P2Y (P2Y1 , P2Y2 , P2Y4 , P2Y6 , P2Y11 , P2Y12 , P2Y13 , and P2Y14 ) receptors (all GPCRs), as well as P2X receptors (ion channels). Receptor activation, especially accompanying physiological stress or damage, creates a temporal sequence of signaling to counteract this stress and either mobilize (P2Rs) or suppress (ARs) immune responses. Thus, modulation of this large signaling family has broad potential for treating chronic diseases. Experimentally determined structures represent each of the three receptor families. We focus on selective purinergic agonists (A1 , A3 ), antagonists (A3 , P2Y14 ), and allosteric modulators (P2Y1 , A3 ). Examples of applying structure-based design, including the rational modification of known ligands, are presented for antithrombotic P2Y1 R antagonists and anti-inflammatory P2Y14 R antagonists and A3 AR agonists. A3 AR agonists are a potential, nonaddictive treatment for chronic neuropathic pain.

Therapeutic Potential of Agonists and Antagonists of A1, A2a, A2b and A3 Adenosine Receptors

Adenosine is a naturally occurring nucleoside and an essential component of the energy production and utilization systems of the body. Adenosine is formed by the degradation of adenosine-triphosphate (ATP) during energy-consuming processes. Adenosine regulates numerous physiological processes through activation of four subtypes of G-protein coupled membrane receptors viz. A1, A2A, A2B and A3. Its physiological importance depends on the affinity of these receptors and the extracellular concentrations reached. ATP acts as a neurotransmitter in both peripheral and central nervous systems. In the peripheral nervous system, ATP is involved in chemical transmission in sensory and autonomic ganglia, whereas in central nervous system, ATP, released from synaptic terminals, induces fast excitatory postsynaptic currents. ATP provides the energetics for all muscle movements, heart beats, nerve signals and chemical reactions inside the body. Adenosine has been traditionally considered an inhibitor of neuronal activity and a regulator of cerebral blood flow. Since adenosine is neuroprotective against excitotoxic and metabolic dysfunctions observed in neurological and ocular diseases, the search for adenosinerelated drugs regulating adenosine transporters and receptors can be important for advancement of therapeutic strategies against these diseases. This review will summarize the therapeutic potential and recent SAR and pharmacology of adenosine and its receptor agonists and antagonists.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Subtype-Selective Fluorescent Ligands as Pharmacological Research Tools for the Human Adenosine A2A Receptor

Among class A G protein-coupled receptors (GPCR), the human adenosine A_2A receptor (hA_2AAR) remains an attractive drug target. However, translation of A_2AAR ligands into the clinic has proved challenging and an improved understanding of A_2AAR pharmacology could promote development of more efficacious therapies. Subtype-selective fluorescent probes would allow detailed real-time pharmacological investigations both in vitro and in vivo. In the present study, two families of fluorescent probes were designed around the known hA_2AAR selective antagonist preladenant (SCH 420814). Both families of fluorescent antagonists retained affinity at the hA_2AAR, selectivity over all other adenosine receptor subtypes and allowed clear visualization of specific receptor localization through confocal imaging. Furthermore, the Alexa Fluor 647-labeled conjugate allowed measurement of ligand binding affinities of unlabeled hA_2AAR antagonists using a bioluminescence resonance energy transfer (NanoBRET) assay. The fluorescent ligands developed here can therefore be applied to a range of fluorescence-based techniques to further interrogate hA_2AAR pharmacology and signaling.

Identification of new potent A1 adenosine receptor antagonists using a multistage virtual screening approach

The use of antagonists for each adenosine receptor (AR) subtype as potent clinical candidates is of growing interest due to their involvement in the treatment of various diseases. The recent resolution of several A₁ and A_2A ARs X-ray structures provides opportunities for structure-based drug design. In this study, we describe the discovery of novel A₁AR antagonists by applying a multistage virtual screening approach, which is based on random forest (RF), e-pharmacophore modeling and docking methods. A multistage virtual screening approach was applied to screen the ChemDiv library (1,492,362 compounds). Among the final hits, 22 compounds were selected for further radioligand binding assay analysis against human A₁AR, and 18 compounds (81.82% success) exhibited nanomolar or low micromolar binding potency (K_i). Then, we selected six compounds (pK_i > 6) to further evaluate their antagonist profile in a cAMP functional assay, and we found that they had low micromolar antagonistic activity (pIC₅₀ = 5.51-6.38) for the A₁AR. Particularly, four of six compounds (pK_i > 6) showed very good affinity (pK_i = 6.11-7.13) and selectively (>100-fold) for A₁AR over A_2AAR. Moreover, the novelty analysis suggested that four of six compounds (pK_i > 6) were dissimilar to existing A₁AR antagonists and hence represented novel A₁AR antagonists. Further molecular docking and molecular dynamics (MD) studies showed that the three selective compounds 15, 20 and 22 were stabilized (RMS_lig value ≤ 2 Å) inside the binding pocket of A₁AR with similar orientations to the docking pose in 100-ns MD simulations, whereas they escaped from the binding area of A_2AAR with larger values of RMS_lig (RMS_lig ≥ 2 Å). We hope that these findings provide new insights into the discovery of drugs targeting A₁AR and facilitate research on new drugs and treatments for A₁AR-related human pathologies.

Insights to the Binding of a Selective Adenosine A3 Receptor Antagonist Using Molecular Dynamic Simulations, MM-PBSA and MM-GBSA Free Energy Calculations, and Mutagenesis

Adenosine A₃ receptor (A₃R) is a promising drug target cancer and for a number of other conditions like inflammatory diseases, including asthma and rheumatoid arthritis, glaucoma, chronic obstructive pulmonary disease, and ischemic injury. Currently, there is no experimentally determined structure of A₃R. We explored the binding profile of O4-{[3-(2,6-dichlorophenyl)-5-methylisoxazol-4-yl]carbonyl}-2-methyl-1,3-thiazole-4-carbohydroximamide (K18), which is a new specific and competitive antagonist at the orthosteric binding site of A₃R. MD simulations and MM-GBSA calculations of the WT A₃R in complex with K18 combined with in vitro mutagenic studies show that the most plausible binding conformation for the dichlorophenyl group of K18 is oriented toward trans-membrane helices (TM) 5, 6 and reveal important residues for binding. Further, MM-GBSA calculations distinguish mutations that reduce or maintain or increase antagonistic activity. Our studies show that selectivity of K18 toward A₃R is defined not only by direct interactions with residues within the orthosteric binding area but also by remote residues playing a significant role. Although V169^5.30 is considered to be a selectivity filter for A₃R binders, when it was mutated to glutamic acid, K18 maintained antagonistic potency, in agreement with our previous results obtained for agonists binding profile investigation. Mutation of the direct interacting residue L90^3.32 in the low region and the remote L264^7.35 in the middle/upper region to alanine increases antagonistic potency, suggesting an empty space in the orthosteric area available for increasing antagonist potency. These results approve the computational model for the description of K18 binding at A₃R, which we previously performed for agonists binding to A₃R, and the design of more effective antagonists based on K18.

Design of novel dopamine D2 and serotonin 5-HT2A receptors dual antagonists toward schizophrenia: An integrated study with QSAR, molecular docking, virtual screening and molecular dynamics simulations

The extrapyramidal side effects of schizophrenia treatment can be significantly reduced by simultaneously targeting dopamine D₂ and serotonin 5-HT_2A receptors. In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) models of D₂ receptor (CoMFA-1, q² = 0.767, r² = 0.969; CoMSIA-1, q² = 0.717, r² = 0.978) and 5-HT_2A receptor antagonists (CoMFA-2, q² = 0.703, r² = 0.946; CoMSIA-2, q² = 0.675, r² = 0.916) were successfully constructed using 35 tetrahydropyridopyrimidinone derivatives. Topomer CoMFA and HQSAR models were then constructed to further validate and supplement above models. Results showed that all models had good predictive power and stability. Contour map analysis revealed that the electrostatic and hydrophobic fields played vital roles in the bioactivity of dual antagonists. Molecular docking and molecular dynamic studies also suggested that the hydrogen bonding, electrostatic and hydrophobic interactions played key roles in the formation of stable binding sites. Meanwhile, several key residues like ASP114, TRP100, PHE389 of dopamine D₂ receptor and ASP134, PHE328, TRP324 of serotonin 5-HT_2A receptor were identified. Based on above findings, seven compounds were obtained through bioisostere replacement and ten compounds were designed by contour map analysis, in which the predicted activity of compounds S6 and DS2 were equivalent to that of the template compound 15. 3D-QSAR and ADMET predictions indicated that all newly designed compounds had great biological activity and physicochemical properties. Moreover, based on the best pharmacophore model, four compounds (Z1, Z2, Z3 and Z4) with new backbones were obtained by virtual screening. Overall, this study could provide theoretical guidance for the structural optimization, design and synthesis of novel dopamine D₂ and serotonin 5-HT_2A receptors dual antagonists. Abbreviations3D-QSARThree-dimensional quantitative structure-activity relationship5-HT_2ARSerotonin 5-hydroxytryptamine 5-HT_2A receptor5-HT_2CRSerotonin 5-hydroxytryptamine 5-HT_2C receptor receptorCADDComputer-aided drug designCoMFAComparative molecular field analysisCoMSIAComparative molecular similarity index analysisD₂RDopamine D(2) receptorGPCRG-protein coupled receptorPLSPartial least squares regressionHQSARHologram quantitative structure-activity relationship. Communicated by Ramaswamy H. Sarma.

Selectivity Challenges in Docking Screens for GPCR Targets and Antitargets

To investigate large library docking's ability to find molecules with joint activity against on-targets and selectivity versus antitargets, the dopamine D2 and serotonin 5-HT2A receptors were targeted, seeking selectivity against the histamine H1 receptor. In a second campaign, κ-opioid receptor ligands were sought with selectivity versus the μ-opioid receptor. While hit rates ranged from 40% to 63% against the on-targets, they were just as good against the antitargets, even though the molecules were selected for their putative lack of binding to the off-targets. Affinities, too, were often as good or better for the off-targets. Even though it was occasionally possible to find selective molecules, such as a mid-nanomolar D2/5-HT2A ligand with 21-fold selectivity versus the H1 receptor, this was the exception. Whereas false-negatives are tolerable in docking screens against on-targets, they are intolerable against antitargets; addressing this problem may demand new strategies in the field.

Breakthrough in GPCR Crystallography and Its Impact on Computer-Aided Drug Design

Recent crystallographic structures of G protein-coupled receptors (GPCRs) have greatly advanced our understanding of the recognition of their diverse agonist and antagonist ligands. We illustrate here how this applies to A_2A adenosine receptors (ARs) and to P2Y₁ and P2Y₁₂ receptors (P2YRs) for ADP. These X-ray structures have impacted the medicinal chemistry aimed at discovering new ligands for these two receptor families, including receptors that have not yet been crystallized but are closely related to the known structures. In this Chapter, we discuss recent structure-based drug design projects that led to the discovery of: (a) novel A₃AR agonists based on a highly rigidified (N)-methanocarba scaffold for the treatment of chronic neuropathic pain and other conditions, (b) fluorescent probes of the ARs and P2Y₁₄R, as chemical tools for structural probing of these GPCRs and for improving assay capabilities, and (c) new more drug-like antagonists of the inflammation-related P2Y₁₄R. We also describe the computationally enabled molecular recognition of positive (for A₃AR) and negative (P2Y₁R) allosteric modulators that in some cases are shown to be consistent with structure-activity relationship (SAR) data. Thus, computational modeling has become an essential tool for the design of purine receptor ligands.

Docking Screens for Dual Inhibitors of Disparate Drug Targets for Parkinson's Disease

Modulation of multiple biological targets with a single drug can lead to synergistic therapeutic effects and has been demonstrated to be essential for efficient treatment of CNS disorders. However, rational design of compounds that interact with several targets is very challenging. Here, we demonstrate that structure-based virtual screening can guide the discovery of multi-target ligands of unrelated proteins relevant for Parkinson's disease. A library with 5.4 million molecules was docked to crystal structures of the A_2A adenosine receptor (A_2AAR) and monoamine oxidase B (MAO-B). Twenty-four compounds that were among the highest ranked for both binding sites were evaluated experimentally, resulting in the discovery of four dual-target ligands. The most potent compound was an A_2AAR antagonist with nanomolar affinity ( K_i = 19 nM) and inhibited MAO-B with an IC₅₀ of 100 nM. Optimization guided by the predicted binding modes led to the identification of a second potent dual-target scaffold. The two discovered scaffolds were shown to counteract 6-hydroxydopamine-induced neurotoxicity in dopaminergic neuronal-like SH-SY5Y cells. Structure-based screening can hence be used to identify ligands with specific polypharmacological profiles, providing new avenues for drug development against complex diseases.

Discovery of Novel Adenosine Receptor Antagonists through a Combined Structure- and Ligand-Based Approach Followed by Molecular Dynamics Investigation of Ligand Binding Mode

An intense effort is made by pharmaceutical and academic research laboratories to identify and develop selective antagonists for each adenosine receptor (AR) subtype as potential clinical candidates for "soft" treatment of various diseases. Crystal structures of subtypes A_2A and A₁ARs offer exciting opportunities for structure-based drug design. In the first part of the present work, Maybridge HitFinder library of 14400 compounds was utilized to apply a combination of structure-based against the crystal structure of A_2AAR and ligand-based methodologies. The docking poses were rescored by CHARMM energy minimization and calculation of the desolvation energy using Poisson-Boltzmann equation electrostatics. Out of the eight selected and tested compounds, five were found positive hits (63% success). Although the project was initially focused on targeting A_2AAR, the identified antagonists exhibited low micromolar or micromolar affinity against A_2A/A₃, ARs, or A₃AR, respectively. Based on these results, 19 compounds characterized by novel chemotypes were purchased and tested. Sixteen of them were identified as AR antagonists with affinity toward combinations of the AR family isoforms (A_2A/A₃, A₁/A₃, A₁/A_2A/A₃, and A₃). The second part of this work involves the performance of hundreds of molecular dynamics (MD) simulations of complexes between the ARs and a total of 27 ligands to resolve the binding interactions of the active compounds, which were not achieved by docking calculations alone. This computational work allowed the prediction of stable and unstable complexes which agree with the experimental results of potent and inactive compounds, respectively. Of particular interest is that the 2-amino-thiophene-3-carboxamides, 3-acylamino-5-aryl-thiophene-2-carboxamides, and carbonyloxycarboximidamide derivatives were found to be selective and possess a micromolar to low micromolar affinity for the A₃ receptor.

Exploring G Protein-Coupled Receptors (GPCRs) Ligand Space via Cheminformatics Approaches: Impact on Rational Drug Design

The primary goal of rational drug discovery is the identification of selective ligands which act on single or multiple drug targets to achieve the desired clinical outcome through the exploration of total chemical space. To identify such desired compounds, computational approaches are necessary in predicting their drug-like properties. G Protein-Coupled Receptors (GPCRs) represent one of the largest and most important integral membrane protein families. These receptors serve as increasingly attractive drug targets due to their relevance in the treatment of various diseases, such as inflammatory disorders, metabolic imbalances, cardiac disorders, cancer, monogenic disorders, etc. In the last decade, multitudes of three-dimensional (3D) structures were solved for diverse GPCRs, thus referring to this period as the "golden age for GPCR structural biology." Moreover, accumulation of data about the chemical properties of GPCR ligands has garnered much interest toward the exploration of GPCR chemical space. Due to the steady increase in the structural, ligand, and functional data of GPCRs, several cheminformatics approaches have been implemented in its drug discovery pipeline. In this review, we mainly focus on the cheminformatics-based paradigms in GPCR drug discovery. We provide a comprehensive view on the ligand- and structure-based cheminformatics approaches which are best illustrated via GPCR case studies. Furthermore, an appropriate combination of ligand-based knowledge with structure-based ones, i.e., integrated approach, which is emerging as a promising strategy for cheminformatics-based GPCR drug design is also discussed.

Structure-based discovery of selective positive allosteric modulators of antagonists for the M2 muscarinic acetylcholine receptor

The orthosteric binding sites of the five muscarinic acetylcholine receptor (mAChR) subtypes are highly conserved, making the development of selective antagonists challenging. The allosteric sites of these receptors are more variable, allowing one to imagine allosteric modulators that confer subtype selectivity, which would reduce the major off-target effects of muscarinic antagonists. Accordingly, a large library docking campaign was prosecuted seeking unique positive allosteric modulators (PAMs) for antagonists, ultimately revealing a PAM that substantially potentiates antagonist binding leading to subtype selectivity at the M₂ mAChR. This study supports the feasibility of discovering PAMs that can convert an armamentarium of potent but nonselective G-protein–coupled receptor (GPCR) antagonist drugs into subtype-selective reagents.

Subtype-selective antagonists for muscarinic acetylcholine receptors (mAChRs) have long been elusive, owing to the highly conserved orthosteric binding site. However, allosteric sites of these receptors are less conserved, motivating the search for allosteric ligands that modulate agonists or antagonists to confer subtype selectivity. Accordingly, a 4.6 million-molecule library was docked against the structure of the prototypical M₂ mAChR, seeking molecules that specifically stabilized antagonist binding. This led us to identify a positive allosteric modulator (PAM) that potentiated the antagonist N-methyl scopolamine (NMS). Structure-based optimization led to compound ’628, which enhanced binding of NMS, and the drug scopolamine itself, with a cooperativity factor (α) of 5.5 and a K_B of 1.1 μM, while sparing the endogenous agonist acetylcholine. NMR spectral changes determined for methionine residues reflected changes in the allosteric network. Moreover, ’628 slowed the dissociation rate of NMS from the M₂ mAChR by 50-fold, an effect not observed at the other four mAChR subtypes. The specific PAM effect of ’628 on NMS antagonism was conserved in functional assays, including agonist stimulation of [³⁵S]GTPγS binding and ERK 1/2 phosphorylation. Importantly, the selective allostery between ’628 and NMS was retained in membranes from adult rat hypothalamus and in neonatal rat cardiomyocytes, supporting the physiological relevance of this PAM/antagonist approach. This study supports the feasibility of discovering PAMs that confer subtype selectivity to antagonists; molecules like ’628 can convert an armamentarium of potent but nonselective GPCR antagonist drugs into subtype-selective reagents, thus reducing their off-target effects.

Identification of Two New Cholesterol Interaction Sites on the A2A Adenosine Receptor

By mole, cholesterol is the most abundant component of animal cell plasma membranes. Many membrane proteins have been shown to be functionally dependent on cholesterol, several of which have also been shown to bind cholesterol at well-defined locations on their membrane-facing surface. In this work, a combination of coarse-grained "Martini" and all-atom simulations are used to identify two, to our knowledge, new cholesterol-binding sites on the A_2A adenosine receptor, a G-protein-coupled receptor that is a target for the treatment of Parkinson's disease. One of the sites is also observed to bind cholesterol in several recent, high-resolution crystal structures of the protein, and in the simulations, interacts with cholesterol only when bound to the inverse agonist ZM241385. Cataloguing cholesterol-binding sites is a vital step in the effort to understand cholesterol-dependent function of membrane proteins. Given that cholesterol content in plasma membranes varies with cell type and on administration of widely prescribed pharmaceuticals, such as statins, understanding cholesterol-dependent function is an important step toward exploiting membrane compositional variation for therapeutic purposes.

Structure-Based Design and Discovery of New M2 Receptor Agonists

Muscarinic receptor agonists are characterized by apparently strict restraints on their tertiary or quaternary amine and their distance to an ester or related center. On the basis of the active state crystal structure of the muscarinic M₂ receptor in complex with iperoxo, we explored potential agonists that lacked the highly conserved functionalities of previously known ligands. Using structure-guided pharmacophore design followed by docking, we found two agonists (compounds 3 and 17), out of 19 docked and synthesized compounds, that fit the receptor well and were predicted to form a hydrogen-bond conserved among known agonists. Structural optimization led to compound 28, which was 4-fold more potent than its parent 3. Fortified by the discovery of this new scaffold, we sought a broader range of chemotypes by docking 2.2 million fragments, which revealed another three micromolar agonists unrelated either to 28 or known muscarinics. Even pockets as tightly defined and as deeply studied as that of the muscarinic reveal opportunities for the structure-based design and the discovery of new chemotypes.

D4 dopamine receptor high-resolution structures enable the discovery of selective agonists

Dopamine receptors are implicated in the pathogenesis and treatment of nearly every neuropsychiatric disorder. Although thousands of drugs interact with these receptors, our molecular understanding of dopaminergic drug selectivity and design remains clouded. To illuminate dopamine receptor structure, function, and ligand recognition, we determined crystal structures of the D4 dopamine receptor in its inactive state bound to the antipsychotic drug nemonapride, with resolutions up to 1.95 angstroms. These structures suggest a mechanism for the control of constitutive signaling, and their unusually high resolution enabled a structure-based campaign for new agonists of the D4 dopamine receptor. The ability to efficiently exploit structure for specific probe discovery-rapidly moving from elucidating receptor structure to discovering previously unrecognized, selective agonists-testifies to the power of structure-based approaches.

Discovery of new GPCR ligands to illuminate new biology

Although a plurality of drugs target G-protein-coupled receptors (GPCRs), most have emerged from classical medicinal chemistry and pharmacology programs and resemble one another structurally and functionally. Though effective, these drugs are often promiscuous. With the realization that GPCRs signal via multiple pathways, and with the emergence of crystal structures for this family of proteins, there is an opportunity to target GPCRs with new chemotypes and confer new signaling modalities. We consider structure-based and physical screening methods that have led to the discovery of new reagents, focusing particularly on the former. We illustrate their use against previously untargeted or orphan GPCRs, against allosteric sites, and against classical orthosteric sites that selectively activate one downstream pathway over others. The ligands that emerge are often chemically novel, which can lead to new biological effects.

Receptor structure-based discovery of non-metabolite agonists for the succinate receptor GPR91

Objective

Besides functioning as an intracellular metabolite, succinate acts as a stress-induced extracellular signal through activation of GPR91 (SUCNR1) for which we lack suitable pharmacological tools.

Methods and results

Here we first determined that the cis conformation of the succinate backbone is preferred and that certain backbone modifications are allowed for GPR91 activation. Through receptor modeling over the X-ray structure of the closely related P2Y1 receptor, we discovered that the binding pocket is partly occupied by a segment of an extracellular loop and that succinate therefore binds in a very different mode than generally believed. Importantly, an empty side-pocket is identified next to the succinate binding site. All this information formed the basis for a substructure-based search query, which, combined with molecular docking, was used in virtual screening of the ZINC database to pick two serial mini-libraries of a total of only 245 compounds from which sub-micromolar, selective GPR91 agonists of unique structures were identified. The best compounds were backbone-modified succinate analogs in which an amide-linked hydrophobic moiety docked into the side-pocket next to succinate as shown by both loss- and gain-of-function mutagenesis. These compounds displayed GPR91-dependent activity in altering cytokine expression in human M2 macrophages similar to succinate, and importantly were devoid of any effect on the major intracellular target, succinate dehydrogenase.

Conclusions

These novel, synthetic non-metabolite GPR91 agonists will be valuable both as pharmacological tools to delineate the GPR91-mediated functions of succinate and as leads for the development of GPR91-targeted drugs to potentially treat low grade metabolic inflammation and diabetic complications such as retinopathy and nephropathy.

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The GPR91 binding site for succinate is identified with an adjacent empty pocket.

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The binding pocket structure is used to identify novel synthetic GPR91 agonists.

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The non-metabolite GPR91 ligands can be used as pharmacological tools and drug leads.

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Novel compounds demonstrate GPR91 control of cytokine expression in M2 macrophages.

A New Class of Fluorinated A2A Adenosine Receptor Agonist with Application to Last-Step Enzymatic [18 F]Fluorination for PET Imaging

The A_2A adenosine receptor belongs to a family of G-coupled protein receptors that have been subjected to extensive investigation over the last few decades. Due to their prominent role in the biological functions of the heart, lungs, CNS and brain, they have become a target for the treatment of illnesses ranging from cancer immunotherapy to Parkinson's disease. The imaging of such receptors by using positron emission tomography (PET) has also been of interest, potentially providing a valuable tool for analysing and diagnosing various myocardial and neurodegenerative disorders, as well as offering support to drug discovery trials. Reported herein are the design, synthesis and evaluation of two new 5'-fluorodeoxy-adenosine (FDA)-based receptor agonists (FDA-PP1 and FDA-PP2), each substituted at the C-2 position with a terminally functionalised ethynyl unit. The structures enable a synthesis of ¹⁸ F-labelled analogues by direct, last-step radiosynthesis from chlorinated precursors using the fluorinase enzyme (5'-fluoro-5'-deoxyadenosine synthase), which catalyses a transhalogenation reaction. This delivers a new class of A_2A adenosine receptor agonist that can be directly radiolabelled for exploration in PET studies.